Perplexity

ABOUT THE ROLE The Connector Platform team builds the data layer that lets Perplexity's agents reach into the world's software. This team owns the systems that turn hundreds of heterogeneous integrations (native, MCP, CLI, first-party, and third-party APIs) into one unified, reliable, well-typed surface that agents can call with confidence. The connector platform is the core layer that forms the knowledge layer for Computer: it is how the agent discovers what tools exist, understands what each one means, decides which to call, and grounds its reasoning in real, permissioned, up-to-date enterprise data. We maintain a knowledge layer above connectors that pushes and pulls context into them, rather than letting each connector hoard org knowledge on its own, making Computer the source of truth for institutional knowledge. Models are commoditizing; grounded, actionable, permissioned access to a customer's real systems is not. When this layer is fast, accurate, and semantically rich, every agent built on top of it gets smarter; when it is weak, no amount of model quality compensates. KEY RESPONSIBILITIES - Own the design and implementation of the connector runtime, the system that registers, hosts, and executes built-in connectors, hosted MCP servers, and CLI-backed tools behind a single agent-facing interface. - Build and extend the semantic layer: tool and entity schemas, capability metadata, relationship modeling, and the mechanisms for capturing and applying organization- and account-specific corrections and knowledge. - Design the tool-discovery and tool-selection surfaces that agents use to find the right connector and call it correctly, optimizing for both model accuracy and context efficiency. - Make agent loops robust: structured results, partial-failure and retry semantics, idempotency, pagination, rate-limit handling, and observability into every tool call an agent makes. - Define authentication, authorization, and credential-isolation patterns for connectors (OAuth flows, BYOK, per-org credential boundaries), partnering with Security and Backend Platform on defense-in-depth. - Build the connector onboarding path (schemas, fixtures, and evaluation suites) so new connectors ship with measurable quality rather than hope, and drive the eval metrics that tell us a connector actually works inside agent loops. - Set the technical bar for connector reliability and operability: SLAs, observability, error-rate monitoring, and incident response for an always-on, high-fan-out integration surface. - Partner with product and AI teams to define clear connector interfaces and integration patterns so new agent capabilities can reliably build on the shared platform. QUALIFICATIONS - Experience designing and building backend systems that run in production (typically 4+ years for mid-level, more for senior and staff). - Strong system design skills, with a track record of building efficient, reliable, and scalable architectures, ideally including API integration, gateway, or platform-style systems with many heterogeneous downstreams. - Strong proficiency in at least one backend language such as Python, Go, or Rust, and the ability to work effectively in a multi-language environment. - Hands-on experience with modern infrastructure (for example AWS, Kubernetes, and related cloud technologies). - Depth in at least one of: OAuth and authorization protocols, API/connector or MCP-server development, schema and semantic modeling, or building tooling and evaluation for LLM-based agents. - Comfort working in security-sensitive areas (auth, authorization, credential isolation) and making pragmatic trade-offs between safety, simplicity, and velocity. - Collaborative mindset and eagerness to solve hard, ambiguous problems alongside other experienced engineers. If you’re excited about this role, we encourage you to apply even if your experience doesn’t match every qualification listed above.

By applying to this role, you will be considered for engineering roles across all teams at Perplexity. WE ARE HIRING BUILDERS TO JOIN OUR HIGHLY LEVERAGED ENGINEERING TEAM FOR CREATING NEW PRODUCTS THAT INNOVATE AND ACCELERATE HUMAN PRODUCTIVITY In 2026, we launched Computer, the defining product for the new era of agentic AI. We’ve scaled beyond the millions of people using Perplexity every day for research, shopping, investing and curiosity into a new paradigm of using AI to transform knowledge into action. As an engineer at Perplexity, your role will be to build products and systems that define how AI empowers humans to think, decide and act. You’ll ship fast and work between product, infrastructure and frontier level advancements in technology. WHY PERPLEXITY IS DIFFERENT - Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious. - Ownership. You identify the problem, design the solution and ship it. - Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. - Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. - Partnership. We amplify each others’ strengths, break down silos, and give selflessly to help our colleagues deliver excellence. WHAT YOU'LL DO - Design, build, and own product and platform systems for Computer - Lead features, projects and products end-to-end, from problem definition to technical design, implementation, and launch. - Hill climb on hard problems, continuously iterating to improve for ourselves and customers. - Partner closely with engineers, product managers, designers, data scientists, and go-to-market teams. - Build systems that take into account the nuances of AI, working with agents, context, evaluation, personalization and the ground truth. QUALIFICATIONS - 4+ years of professional software engineering experience. - Strong experience in at least one general-purpose programming language such as Python, Go, TypeScript, JavaScript, Swift, Rust, Java, or C++. - Experience building and operating production systems at a meaningful scale. - Strong product judgment and the ability to translate user problems into simple, effective technical solutions. - Genuine interest and adoption of AI products and willingness to learn quickly. NICE TO HAVE - Experience with LLMs, agents, retrieval systems, personalization, or evaluation frameworks. - Time spent at a fast-growing startup or on a high-ownership engineering team. - Founded a company or initiative. OUR MISSION Perplexity’s mission is to power curiosity. Curious people are the people who drive change in the world. Driving change is a continuous cycle of learning, building, and integrating. Learn: curious people constantly learn new things by asking more. They question the status quo in their own expertise and they constantly learn outside of it. Research is essential to them and never ending. Build: curious people make and create things, to show the world their new answers to problems no one else ever questioned. They take action on what they’ve learned. Makers need tools to create their products, their companies, their reality. Integrate: they must interact with the world as it is to drive change and adoption. True leaders do not simply build something and hope. They must have armies of agents and workers who can constantly work in millions of small ways. Repeat. For curious people this is a cycle that never ends.

Perplexity is seeking a highly skilled, experienced and hands-on Offensive Security Engineer to join our dynamic security team, taking an adversarial approach to hardening Perplexity's infrastructure, applications, and AI systems. You'll plan and execute red team operations, penetration tests, and attack simulations across our cloud infrastructure, web and mobile applications, AI/ML pipeline, and corporate environment—finding real vulnerabilities before adversaries do and working directly with engineering teams to drive remediation. Responsibilities - Plan and execute red team and purple team engagements simulating advanced threat actors across cloud infrastructure (AWS, Kubernetes), endpoints, and application surfaces - Conduct continuous penetration testing of web applications, APIs, mobile clients, browser extensions, cloud infrastructure, and internal services - Assess AI/ML-specific attack surfaces including prompt injection, model exfiltration, agent abuse, tool-use exploitation, and MCP security boundaries - Develop and maintain custom offensive tooling, exploits, and automation to improve the efficiency and coverage of security testing - Perform open-scope adversary simulations that test detection and response capabilities end to end, collaborating closely with the defensive security team - Drive threat modeling sessions with engineering teams to identify and prioritize attack vectors in new features and architectures - Deliver clear, actionable findings to both technical and executive audiences; partner with engineering to validate remediations - Contribute to the security of CI/CD pipelines, supply chain integrity, and secrets management through offensive assessment - Stay current on emerging attack techniques, vulnerability research, and adversary tradecraft; bring external perspective into Perplexity's security strategy Qualifications - 5+ years of hands-on experience in offensive security, red teaming, or penetration testing - Deep technical expertise in at least two of: cloud security (AWS/GCP/Azure), web/API application security, Kubernetes and container security, macOS/Linux endpoint security, network penetration testing, or CI/CD pipeline security - Track record of discovering impactful vulnerabilities or developing novel attack techniques in production environments - Strong programming and scripting skills in Python, Go, or similar languages; comfortable writing custom tooling and exploits - Experience with industry-standard offensive tools (Burp Suite, Cobalt Strike / Sliver / Mythic, Metasploit, BloodHound, nuclei, etc.) and ability to operate beyond them - Excellent written and verbal communication; able to translate complex technical findings into clear risk narratives - Experience assessing AI/ML systems, LLM applications, or agentic workflows for security vulnerabilities - Bonus: Published security research, conference talks (DEF CON, Black Hat, BSides), CVE credits, or meaningful bug bounty contributions

Perplexity is seeking an experienced Machine Learning Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. Responsibilities - Relentlessly push search quality forward—through models, data, tools, or any other leverage available - Architect and build core components of our search platform and model stack - Train and evaluate retrieval, ranking and classification models, including LLMs - Deploy models - from boosting to LLMs - in a scalable and performant way - Build and optimize RAG pipelines for grounding and answer generation - Collaborate with Data, AI, Infrastructure and Product teams to ensure fast and high quality delivery Qualifications - Deep understanding of search and retrieval systems, including quality evaluation principles and metrics - Proven track record with large-scale search or recommender systems - Self-driven, with a strong sense of ownership and execution - Minimum of 5 years of working on search or recsys-related projects

Perplexity is AI for people who expect more. This role brings that same standard to how our data team works - with AI at the center of everything we do. We're looking for someone who's been a great data scientist, analytics engineer, or data engineer - the kind of person who knows which metric actually matters, who can design an A/B test that answers the real question, who's gone deep on a data model because something didn't add up - and who has decided that the highest-leverage thing they can do next is build AI systems that fundamentally change how data science gets done. Not another text-to-SQL bot. Not another dashboard. You'll build AI agents that conduct full analyses autonomously - forming hypotheses, writing and running queries, interpreting results, and drafting recommendations. You'll make the entire data warehouse AI-readable so any system can query it accurately. You'll create self-healing pipelines that detect and fix data issues before anyone notices. You'll build the infrastructure that turns a small data team into one that operates at 10x its size. You'll join a data team that's already using AI across its workflows - but we know there's a much bigger opportunity ahead. We have buy-in from leadership to make it happen. Now we're building a team dedicated to taking what we've started and turning it into something world-class: scalable systems, new tools, and an AI-native way of working that doesn't just make us world-class - but pushes the entire industry forward. WHAT YOU'LL DO - Accelerate the AI-native data workflow - the team is already working this way. You'll take what's working and turn it into repeatable systems, scalable tools, and patterns that the data team and the entire company can adopt - Build AI agents that do data science - not just answer SQL questions, but conduct end-to-end analyses: explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations - Make the warehouse AI-readable - build the semantic layer, context, and retrieval infrastructure that lets any AI system (internal or product) query Perplexity's data accurately and reliably - Automate the data lifecycle - self-healing pipelines, automated dbt model generation and validation, data quality agents that detect, diagnose, and fix issues autonomously - Ship AI-powered experiment analysis - agents that interpret A/B test results, flag statistical issues, and draft ship/no-ship recommendations for product teams - Own the full lifecycle - from identifying the highest-leverage problem, to prototyping with LLMs, to iterating on accuracy and UX, to production deployment and monitoring - Turn the data team into a product team - build internal data products that stakeholders across the company actually use daily, replacing ad-hoc requests with self-serve AI interfaces WHAT WE'RE LOOKING FOR - 6-8+ years in data science, analytics engineering, or a related role - you've been in the data trenches - Strong product sense - you've worked closely with product and business teams, you understand what drives user behavior, and you have good instincts for what to measure and what to build - Deep SQL expertise - you think in SQL, you've built data models, you know your way around a warehouse - Pipeline experience - you've built and maintained data pipelines, worked with dbt, dealt with data quality issues firsthand - Enough software engineering chops to be dangerous - you can build and ship a working tool in Python, not just a notebook. You can wrangle APIs, deploy a service, write code that other people can maintain. You're not a SWE, but you're not afraid of production - Genuinely excited about AI - you've been building with LLMs on your own time. You have opinions about which models are good at what. You've tried building agents, RAG systems, or AI-powered workflows. You follow the space obsessively because you think it's going to change everything - including how data teams work - Builder mentality - you see a manual process and you can't help but automate it. You ship fast and iterate - Autonomy - this is a new function. You'll define the roadmap as much as execute it BONUS - Experience with dbt (building and maintaining production models) - Snowflake administration and optimization - You've built Slack bots, internal CLI tools, or developer productivity tools that people actually used - Background in AI agent frameworks - Experience with BI tools - you know what's worth automating because you've done the manual version - A/B testing and experimentation - you've designed experiments and analyzed results - Early-stage startup experience WHY THIS ROLE - Set the standard for the industry - the team is already using AI across its work. You'll be the one who turns that into something other data orgs look to as the benchmark - Recursive AI - Perplexity builds an AI answer engine for the world. You'll build one for the company. Few places offer this kind of alignment between the product and the work - Frontier models, day one - you're at an AI company with access to frontier infrastructure and people who deeply understand what's possible - Massive leverage - the systems you build will multiply the output of every data team member and every stakeholder who needs data - Direct impact - small team, no layers of approval. Idea to shipped system in days, not quarters

Perplexity is AI for people who expect more. This role brings that same standard to how our data team works - with AI at the center of everything we do. We're looking for someone who's been a great data scientist, analytics engineer, or data engineer - the kind of person who knows which metric actually matters, who can design an A/B test that answers the real question, who's gone deep on a data model because something didn't add up - and who has decided that the highest-leverage thing they can do next is build AI systems that fundamentally change how data science gets done. Not another text-to-SQL bot. Not another dashboard. You'll build AI agents that conduct full analyses autonomously - forming hypotheses, writing and running queries, interpreting results, and drafting recommendations. You'll make the entire data warehouse AI-readable so any system can query it accurately. You'll create self-healing pipelines that detect and fix data issues before anyone notices. You'll build the infrastructure that turns a small data team into one that operates at 10x its size. You'll join a data team that's already using AI across its workflows - but we know there's a much bigger opportunity ahead. We have buy-in from leadership to make it happen. Now we're building a team dedicated to taking what we've started and turning it into something world-class: scalable systems, new tools, and an AI-native way of working that doesn't just make us world-class - but pushes the entire industry forward. WHAT YOU'LL DO - Accelerate the AI-native data workflow - the team is already working this way. You'll take what's working and turn it into repeatable systems, scalable tools, and patterns that the data team and the entire company can adopt - Build AI agents that do data science - not just answer SQL questions, but conduct end-to-end analyses: explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations - Make the warehouse AI-readable - build the semantic layer, context, and retrieval infrastructure that lets any AI system (internal or product) query Perplexity's data accurately and reliably - Automate the data lifecycle - self-healing pipelines, automated dbt model generation and validation, data quality agents that detect, diagnose, and fix issues autonomously - Ship AI-powered experiment analysis - agents that interpret A/B test results, flag statistical issues, and draft ship/no-ship recommendations for product teams - Own the full lifecycle - from identifying the highest-leverage problem, to prototyping with LLMs, to iterating on accuracy and UX, to production deployment and monitoring - Turn the data team into a product team - build internal data products that stakeholders across the company actually use daily, replacing ad-hoc requests with self-serve AI interfaces WHAT WE'RE LOOKING FOR - 6-8+ years in data science, analytics engineering, or a related role - you've been in the data trenches - Strong product sense - you've worked closely with product and business teams, you understand what drives user behavior, and you have good instincts for what to measure and what to build - Deep SQL expertise - you think in SQL, you've built data models, you know your way around a warehouse - Pipeline experience - you've built and maintained data pipelines, worked with dbt, dealt with data quality issues firsthand - Enough software engineering chops to be dangerous - you can build and ship a working tool in Python, not just a notebook. You can wrangle APIs, deploy a service, write code that other people can maintain. You're not a SWE, but you're not afraid of production - Genuinely excited about AI - you've been building with LLMs on your own time. You have opinions about which models are good at what. You've tried building agents, RAG systems, or AI-powered workflows. You follow the space obsessively because you think it's going to change everything - including how data teams work - Builder mentality - you see a manual process and you can't help but automate it. You ship fast and iterate - Autonomy - this is a new function. You'll define the roadmap as much as execute it BONUS - Experience with dbt (building and maintaining production models) - Snowflake administration and optimization - You've built Slack bots, internal CLI tools, or developer productivity tools that people actually used - Background in AI agent frameworks - Experience with BI tools - you know what's worth automating because you've done the manual version - A/B testing and experimentation - you've designed experiments and analyzed results - Early-stage startup experience WHY THIS ROLE - Set the standard for the industry - the team is already using AI across its work. You'll be the one who turns that into something other data orgs look to as the benchmark - Recursive AI - Perplexity builds an AI answer engine for the world. You'll build one for the company. Few places offer this kind of alignment between the product and the work - Frontier models, day one - you're at an AI company with access to frontier infrastructure and people who deeply understand what's possible - Massive leverage - the systems you build will multiply the output of every data team member and every stakeholder who needs data - Direct impact - small team, no layers of approval. Idea to shipped system in days, not quarters

Perplexity is revolutionizing how people discover and interact with information through AI-powered search and knowledge tools. As we expand our global footprint, we're establishing a strategic presence in London to drive innovation and growth across Europe. The Role: We're seeking an exceptional Site Lead to establish and scale our London office. This is a unique opportunity to shape Perplexity's presence in one of the world's leading tech hubs, building teams and culture from the ground up while driving technical excellence in infrastructure and AI systems. As Site Lead, you'll serve as the face of Perplexity in London, responsible for building our technical organization, fostering a world-class engineering culture, and directly managing one or more infrastructure teams. You'll report to senior leadership and work cross-functionally with teams across our global footprint. The individual in this role will manage teams in LON themselves while also facilitating. Responsibilities: Site Leadership & Culture - Establish and lead Perplexity's London office, setting the cultural foundation and operating principles - Build a collaborative, high-performance engineering culture that aligns with Perplexity's values while embracing the strengths of the London tech ecosystem - Serve as the primary point of contact for all London-based activities and represent the site in company-wide strategic discussions - Partner with People/HR, Finance, and Operations to ensure seamless site operations - Drive local community engagement, partnerships, and Perplexity's brand presence in the London and European tech community Technical Leadership: - Directly manage and mentor one or more infrastructure or AI infrastructure teams in London (5-15+ engineers) - Set technical direction and strategy for London-based infrastructure initiatives in alignment with company-wide goals - Drive architectural decisions and technical excellence across teams - Ensure robust systems for deployment, monitoring, scalability, and reliability of infrastructure supporting AI/ML workloads - Collaborate with engineering leaders globally to align on technical standards, best practices, and cross-site initiatives Team Building & Talent: - Build and scale high-performing infrastructure and AI infrastructure teams through strategic hiring - Develop and execute talent acquisition strategy for the London site in partnership with recruiting - Create career development frameworks and growth opportunities for engineers - Foster technical mentorship and knowledge sharing across teams and sites Cross-functional Collaboration: - Partner with Product, Engineering, and Research teams globally to understand infrastructure needs and deliver solutions - Coordinate with other site leads and engineering leaders to ensure effective cross-site collaboration - Contribute to company-wide infrastructure strategy and roadmap planning - Facilitate knowledge transfer and best practice sharing across global teams Qualifications: Required - 10+ years of experience in software engineering with 5+ years in infrastructure, cloud infrastructure, or AI infrastructure roles - 3+ years of people management experience, including building and scaling teams - Proven track record of establishing or significantly growing an engineering site or office - Deep technical expertise in distributed systems, cloud platforms (AWS, GCP, or Azure), and infrastructure automation - Experience with infrastructure supporting large-scale AI/ML systems, including: - GPU infrastructure and orchestration - ML training and inference pipelines - Model serving and deployment at scale - Strong understanding of modern infrastructure technologies: Kubernetes, Terraform, container orchestration, CI/CD systems - Demonstrated ability to set technical vision and drive execution across multiple teams - Excellent communication and stakeholder management skills - Experience working in fast-paced, high-growth technology companies - Passion for building inclusive, diverse, and high-performing teams Preferred - Experience at companies focused on AI/ML, search, or large-scale consumer applications - Previous experience as a site lead, office lead, or similar multi-team leadership role - Background in building infrastructure for LLM training or inference - Contributions to open-source infrastructure or AI infrastructure projects - Experience scaling teams from 0 to 20+ engineers - Active involvement in the London or European tech community - MBA or advanced technical degree What Success Looks Like: 30 Days - Deep understanding of Perplexity's infrastructure, technology stack, and organizational structure - Established relationships with key stakeholders across engineering, product, and leadership - Initial hiring plan and culture strategy for London site established - Help the Search, API, AI and Infra teams build out their hiring pipelines 90 Days - Core infrastructure team established and ramping in London - Clear technical roadmap and priorities defined for London-based teams - Site culture and operating rhythms established (team meetings, all-hands, cross-site syncs) - London office actively participating in company-wide infrastructure initiatives 1 Year - London site operating as a high-functioning hub with 15-30+ engineers - Infrastructure teams delivering measurable impact on system reliability, performance, and scalability - Strong talent brand established in London market with healthy hiring pipeline - London recognized internally as a strategic site contributing to Perplexity's technical leadership Why Join Perplexity - Ground-floor opportunity to build and lead a strategic site for a fast-growing AI company - Work on cutting-edge AI infrastructure challenges at massive scale - Shape the culture and technical direction of an entire office - Competitive compensation including equity - Comprehensive benefits package - Flexible work environment - Opportunity to make a significant impact on how millions of people access and interact with information Location: London, United Kingdom (Hybrid) Perplexity is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

ABOUT THE ROLE The Data Platform team owns the end-to-end data lifecycle at Perplexity, from ingestion through processing, storage, and serving, powering product features, analytics, experimentation, AI workloads, and the company’s data lake. The team defines the architecture for batch and streaming systems, the orchestration and observability stack, and a self-serve data platform, while thoughtfully combining platforms such as Databricks and Snowflake with open-source technologies including Spark, Kafka, Flink, Airflow, Dagster, dbt, Iceberg, Delta Lake, and ClickHouse. In this senior/staff role, you will shape architecture, set standards, and drive the long-term technical direction of Perplexity’s data ecosystem. KEY RESPONSIBILITIES - Design and operate large-scale batch and streaming data pipelines that directly power Perplexity product features, AI training and evaluation workflows, analytics, and experimentation. - Build event-driven and streaming systems (Kafka, Kinesis, PubSub, or similar) for real-time ingestion, transformation, and delivery, alongside batch frameworks for backfills, aggregations, and offline computation. - Lead the architecture of data orchestration using tools like Airflow or Dagster, owning scheduling, dependency management, retries, SLAs, and end-to-end observability for critical data flows. - Set and enforce guarantees for data correctness, freshness, lineage, and recoverability, designing systems that handle rapid scale growth, partial failures, and evolving schemas without disrupting AI workloads or product experiences. - Build self-serve data platforms that let engineers, data scientists, and analysts safely discover data, define contracts, and create and operate their own pipelines with minimal friction. - Improve developer experience through better abstractions, opinionated paved paths, and standards for data modeling, testing, validation, and deployment, treating the data platform as a product used by many teams. - Drive architectural decisions across storage, compute, orchestration, and data APIs, partnering closely with product engineering and data science to align the data ecosystem with Perplexity’s roadmap. - Mentor engineers, review designs, and raise the technical bar for data infrastructure through thoughtful feedback, documentation, and hands-on collaboration. QUALIFICATIONS - 5+ years (Senior) or 8+ years (Staff) of software engineering experience. - Strong experience building production data infrastructure systems. - Hands-on experience with batch and/or streaming data processing at scale. - Deep familiarity with data orchestration systems (Airflow, Dagster, or similar). - Proficiency in Python and at least one additional backend language (Go, TypeScript, etc.). - Strong systems thinking around reliability, latency, cost, and complexity tradeoffs. - Experience supporting ML/AI workflows, training pipelines, or evaluation systems. - Familiarity with data quality, lineage, observability, and governance tooling. - Prior ownership of internal platforms used by many teams. If you’re excited about this role, we encourage you to apply even if your experience doesn’t match every qualification listed above.

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search, and specialized datasets, combined with both frontier and in-house models, create the best possible experience for our users. As our product evolves, our evaluations must remain fast, accurate, and actionable. In this role, you will build the data flywheel that serves teams across Perplexity. RESPONSIBILITIES - Build the systems and pipelines that enable Search, Product, and other teams to independently access and utilize reliable eval verdicts without bottlenecks - Take ownership of the "evals-to-product" loop, autonomously determining the best way to turn raw signals into durable datasets that power decision-making across the company - Build a robust simulator pipeline capable of replaying user interactions with the product in formats legible to LLMs and VLMs, reflecting product changes as they are shipped - Maintain data trust by implementing monitoring, lineage, and quality checks, ensuring downstream consumers can rely on the results implicitly - Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality QUALIFICATIONS - 3+ years of software engineering experience shipping production systems - Strong proficiency in Python and SQL with the ability to write production-grade, maintainable code - Experience with big data systems including distributed compute and large-scale storage - Solid fundamentals in data modeling, system design, and debugging distributed systems - Experience with AWS and lakehouse ecosystems like Databricks or Spark - Comfortable with agentic coding workflows and using AI-assisted development tools to iterate faster PREFERRED QUALIFICATIONS - Data engineering background including pipelines, orchestration, and warehousing patterns - Familiarity with LLM/VLM interfaces, tokenization, structured formats, and multimodal payloads - Experience with evaluation platforms, experimentation systems, or machine learning infrastructure - Prior work supporting customer-facing products at scale

In 2026, we launched Computer, the defining product for the new era of agentic AI. Millions of people now use Perplexity to transform knowledge into action, and every action an agent takes is metered, budgeted, and settled through the billing platform this role owns. Perplexity Computer is one of the defining products of the new era of agentic AI. Millions of people use Perplexity to transform knowledge into action, and the Capabilities team sits at the intersection of frontier AI research and product innovation, building the foundations that shape how users and agents solve increasingly complex tasks. As every major breakthrough in AI models creates new possibilities, the Capabilities team is responsible for turning frontier AI breakthroughs into reusable product capabilities. We are often the first to evaluate emerging model capabilities, define how they should appear in the product, and transform them into reliable, production grade experiences for both users and agents. This is a highly leveraged role with broad ownership at the intersection of frontier AI research, agent systems, platform engineering, and product innovation. Tech Stack: Python | Go | PostgreSQL | DynamoDB | AWS | TypeScript | React WHY PERPLEXITY IS DIFFERENT - Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious. - Ownership. You identify the problem, design the solution and ship it. - Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. - Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. - Partnership. We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence. WHAT YOU'LL DO - Investigate emerging frontier-model behaviors and identify opportunities to turn them into product capabilities. Help define how advances in reasoning, planning, memory, learning, and agent collaboration appear in the product. - Build and evolve Skills, Workflows, and Artifacts into a coherent ecosystem. Shape the architecture, abstractions, and product experiences that enable both users and agents to compose increasingly sophisticated solutions for complex real-world tasks. - Own capabilities end-to-end, from user-facing products and interfaces to backend services, evaluation, data models, and production operations. - Build scalable platform infrastructure and backend systems that power capability execution, sharing, and lifecycle management across high volume user and agent interactions. - Collaborate closely with PM, Design, Data Science, Sales, Research, to identify high-impact opportunities in both consumer and enterprise customer experiences, validate emerging capabilities, and translate complex agent behaviors into simple, reliable product experiences. - Set technical direction on ambiguous problems and raise the bar through design reviews, mentorship, and technical leadership. QUALIFICATIONS - Typically 8+ years of professional software engineering experience, with a track record of owning and delivering complex products or systems from conception to production. Exceptional candidates with less experience and an outstanding record of impact are encouraged to apply. - Strong full-stack engineering fundamentals, including experience building user-facing products, backend services, and scalable distributed systems that serve high traffic and large user bases. - Strong product judgment and instincts; you turn vague needs into simple, reliable systems and ship without waiting for perfect specs. - Comfort with data-informed decisions; you define the metrics and evals that prove a system works, and iterate on them. - Experience designing abstractions, platforms, or reusable systems that enable other engineers, users, or automated systems to accomplish complex tasks. - Strong execution: you can take ambiguous product needs, break them down, and ship durable systems with clear user impact. - Genuine interest in frontier AI capabilities, agent systems, and excitement for rapidly exploring, evaluating, and productizing new model behaviors. NICE TO HAVE - Experience building agentic systems (tool calling, subagents, long-running or autonomous task execution). - Experience building developer platforms or reusable-capability primitives (SDKs, plugin systems, workflow engines). - Experience with evaluation, benchmarking, or quality systems for ML/LLM-powered products. - Time spent at a fast-growing startup or on a high-ownership engineering team.

Perplexity is seeking experienced ML engineers to design, build, and optimize the recommendation systems that power core experiences on Perplexity. Perplexity builds AI for those who expect more. Our products are designed to help people find answers, make their most consequential decisions, and complete increasingly ambitious work. Across these use cases, the Perplexity experience must feel deeply personal. Great recommendations and user understanding are central to delighting and delivering for each user. To do this, we are reimagining recommendation systems for the LLM era. Our goal is to combine the intelligence of frontier LLMs, the personalization context that comes from real product usage, and the continual learning capabilities of modern recommendation systems. We build systems that draw on past context and connected data sources to deeply understand each user's needs and recommend the actions that help them get the most out of Perplexity. WHY PERPLEXITY IS DIFFERENT - Craftsmanship. We build high quality, tasteful products targeting both AI native and AI curious users. - Ownership. You identify the problem, design the solution and ship it. - Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. - Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. - Partnership. We amplify each others’ strengths, break down silos, and give selflessly to help our colleagues deliver excellence. WHAT YOU'LL DO - Own the personalization and ranking behind key product surfaces to make Perplexity more useful and drive impact on core user and business metrics. - Build user modeling that captures intent, preference, and propensity, and powers more relevant, more personalized experiences. - Design the decision layer that balances competing objectives to produce the best overall experience for the user. - Build the data and evaluation foundations that let these systems learn and improve with usage. - Help shape the technical direction of ranking, recommendations, and personalization at Perplexity. WHAT WE'RE LOOKING FOR - Deep, hands-on experience building production recommendation, ranking, or personalization systems at scale. - Strong ML fundamentals, covering areas such as engagement modeling, model calibration, offline and online metrics, and online experimentation. - Experience integrating LLMs into ranking, retrieval, or personalization pipelines. - Taste and judgment for how personalization should work in an LLM-native product, and curiosity about reimagining it from first principles. - For tech leadership roles, we will also look for prior experience setting technical direction for recommendation/ranking projects. NICE TO HAVE - Experience with large-scale ranking and training infrastructure (multi-stage retrieval and ranking, feature stores, real-time serving). - Background in user understanding, feed ranking, notifications, growth, or lifecycle modeling. OUR MISSION Perplexity’s mission is to power curiosity. Curious people are the people who drive change in the world. Driving change is a continuous cycle of learning, building, and integrating. Learn: curious people constantly learn new things by asking more. They question the status quo in their own expertise and they constantly learn outside of it. Research is essential to them and never ending. Build: curious people make and create things, to show the world their new answers to problems no one else ever questioned. They take action on what they’ve learned. Makers need tools to create their products, their companies, their reality. Integrate: they must interact with the world as it is to drive change and adoption. True leaders do not simply build something and hope. They must have armies of agents and workers who can constantly work in millions of small ways. Repeat. For curious people this is a cycle that never ends.

ABOUT PERPLEXITY AI Perplexity is an AI-powered answer engine built to serve the world’s curiosity with fast, trustworthy answers grounded in the live web and backed by clear citations. It combines multiple leading models with real-time search to synthesize up-to-date, source-linked responses instead of traditional search results. On top of this foundation, Perplexity is rolling out Computer, a general-purpose AI worker that can use software like a human to research, build, and execute end-to-end workflows for users. ABOUT THE ROLE Perplexity is building AI systems that integrate directly into how enterprises operate. Our API Platform powers search, retrieval, and automation across structured and unstructured data, while Perplexity Computer extends this into the execution of an AI system that navigates tools, interacts with applications, and completes multi-step workflows. Together, these platforms form a new integration layer, connecting models, data, and enterprise systems into end-to-end workflows. We’re looking for Forward Deployed Engineers to work directly with customers to design and deploy these integrations in production. You’ll embed with teams, connect Perplexity into their existing stack, and build systems that automate real work. This role spans two closely connected areas: - API Platform: integrating search, retrieval, and AI capabilities into enterprise systems - Perplexity Computer: deploying agentic workflows that operate across tools, applications, and data Perplexity’s platforms are redefining how AI is integrated into enterprise operations. You’ll work on cutting-edge AI systems, building end-to-end workflows and integrations that solve real business problems at scale. This is a unique opportunity to influence both product and adoption, embed AI deeply in enterprise workflows, and shape the future of how organizations operationalize AI. KEY RESPONSIBILITIES - Design, build, and deploy end-to-end integrations between Perplexity and enterprise systems (data platforms, internal tools, SaaS applications), translating business workflows into production-grade AI systems - Work directly with customer teams to embed AI into existing processes, owning deployments from initial architecture through production rollout and ongoing optimization - Develop and operationalize integrations using APIs, event-driven architectures, and workflow orchestration, including deploying Perplexity Computer for multi-step, agent-driven workflows across tools and environments - Design and build production systems that combine retrieval, reasoning, and execution across enterprise environments, applying deep expertise in LLM capabilities, implementation patterns, and the AI stack to drive performance, security, and customer impact - Debug and resolve issues across APIs, infrastructure, and external dependencies, ensuring reliability, performance, and scalability in production - Prototype new integration patterns and build reusable architectures that accelerate adoption across customers - Partner with Sales and Product to unlock new use cases, drive expansion, and translate deployment learnings into product and platform improvements QUALIFICATIONS - 5+ years of experience in software engineering, forward deployed engineering, solutions engineering, or similar roles, with a track record of building and shipping production systems in customer-facing environments - Strong programming ability in Python (plus one of JavaScript/TypeScript, Java, etc.) with experience developing integrations, prototypes, and scalable applications - Deep experience with APIs and distributed systems, including authentication, latency optimization, and debugging across complex, multi-system environments - Production experience building LLM-powered systems, including prompt engineering, agent workflows, evaluation, and deploying AI systems at scale - Proven ability to design and implement automated, end-to-end workflows that integrate across enterprise systems and replace manual processes - High ownership and ability to operate in ambiguous environments, with strong system design, rapid prototyping skills, and end-to-end execution - Excellent communication and collaboration skills, with experience working cross-functionally and engaging both technical teams and executive stakeholders NICE-TO-HAVES - Experience with search systems, retrieval-augmented generation (RAG), or AI/ML APIs - Background in developer tools, platform engineering, or high-scale/low-latency system design - History of working at startups or small teams, owning customer projects end-to-end - Experience with enterprise IT systems or AI deployment patterns in regulated industries (finance, healthcare, life sciences) If you’re excited about this role, we encourage you to apply even if your experience doesn’t match every qualification listed above.

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search, and specialized datasets, combined with both frontier and in-house models, create the best possible experience for our users. As a Data Scientist/Engineer on this team, you will derive online signals from user interactions to bridge the gap between changes in answer quality and observed user behavior. RESPONSIBILITIES - Discover and validate online signals from user interactions that serve as reliable proxies for true answer quality - Design and implement novel online metrics to be tracked both in A/B testing and on product health dashboards, ensuring alignment with ground-truth evaluations - Analyze experimental results to validate these metrics, ensuring they accurately predict user satisfaction and drive product decisions - Build and maintain the data pipelines that calculate these metrics at scale, delivering actionable quality signals to Search, Product, and model training teams - Communicate findings and bring clarity through close collaboration with Product and Search teams - Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality QUALIFICATIONS - MS in a technical field or equivalent experience - 4+ years of experience working as a Data Scientist, Analytics Engineer, or related role - Experience working on search, recommendation, or LLM-based products, with an emphasis on designing online metrics and analyzing A/B experiments - Strong proficiency in Python and SQL (expected to write production-grade code) - Deep knowledge of statistical analysis - Experience with Business Intelligence (BI) tools for visualization and reporting - Comfortable with agentic coding workflows and using AI-assisted development tools to iterate faster PREFERRED QUALIFICATIONS - Proficiency with Apache Spark and Databricks - Experience with the development or validation of LLM-as-a-judge systems - Prior work supporting customer-facing products at scale

Perplexity is an AI-powered answer engine used to solve billions of queries every month. We build accurate, trustworthy AI that powers decision-making for curious people and organizations worldwide. We are looking for an experienced Enterprise Customer Support Specialist who can marry deep product expertise with a passion for scaling world-class support through automation. You will be the primary advocate for our Enterprise Pro customers, helping them maximize value, troubleshooting complex issues, and feeding their insights straight into future product development. RESPONSIBILITIES - Work directly with enterprise customers—via tickets, Slack, and sometimes calls—to diagnose and resolve their most complex technical and product questions, acting as the “last line of defense” before Product and Engineering step in. - Leverage Perplexity’s own AI tooling and workflow automations to democratize world-class support at scale, continuously identifying opportunities to replace repetitive tasks with agentic, self-service solutions that feel personalized. - Build durable relationships with Enterprise users, advising on best practices, capturing structured feedback, and championing customer needs in roadmap discussions. - Own end-to-end troubleshooting: reproduce issues, isolate root causes, collaborate with engineers, and communicate clear, low-jargon explanations. - Design and maintain detailed use-case flows, playbooks, and internal runbooks that empower teammates and customers to solve recurring challenges faster. - Create and update external documentation (FAQs, help center, guides, tutorials) and internal knowledge bases to ensure information is discoverable and up-to-date. - Track and report support KPIs (response time, CSAT, resolution rates) and propose data-driven process improvements. - Participate in an on-call rotation—including some holidays or weekends—to guarantee timely global coverage. REQUIREMENTS - Minimum 3+ years in B2B enterprise customer support with exposure to U.S. and E.U. markets, or similar fast-paced tech environments. - Hands-on experience prompting large-language models, plus a solid grasp of AI fundamentals (tokens, context windows, embeddings, latency vs. cost trade-offs, etc.). - Ability to translate complex technical concepts—APIs, SSO/SAML, cloud integrations—into clear, actionable guidance for non-technical stakeholders. - Demonstrated strength in critical thinking, rapid context-switching, and ruthless prioritization when juggling multiple escalations1 https://openai.com/careers/ai-technical-support-specialist/. - Proficiency with modern support platforms (Intercom, Zendesk, Jira) and basic data-analysis tools (e.g., SQL, Looker, Snowflake). - Exceptional written and spoken English; business-level fluency in at least one additional language such as Spanish, French, or German is strongly preferred. - Passion for continuous learning, high ownership, and a “do-what-it-takes” mindset in ambiguous situations. BONUS POINTS - Prior experience supporting AI, search, or knowledge-management products. - Familiarity with payment platforms (Stripe), enterprise identity (SSO/OAuth), and API integrations. - Knowledge of enterprise cloud-storage ecosystems (Google Drive, SharePoint, Dropbox) and data-governance best practices. WHY PERPLEXITY? - Impact at Scale – Your work directly influences hundreds of enterprise clients and millions of end-users at an unprecedented scale to encourage personalized support - Velocity & Ownership – Ship improvements quickly in a culture that values curiosity, speed, and quality - Cutting-Edge Tech – Operate at the forefront of applied LLMs and help invent how AI can redefine customer support itself.

Perplexity is an AI-powered answer engine used to solve billions of queries every month. We build accurate, trustworthy AI that powers decision-making for curious people and organizations worldwide. As we scale across Asia-Pacific, we are investing in a regional team that can deliver the same world-class experience our Enterprise Pro customers expect, in their language, in their time zone, and to their service standards. The Perplexity APAC team is the frontline for delivering Perplexity AI's industry-leading search capabilities to enterprise clients and individual users across Asia-Pacific markets. The team partners closely with Product, Engineering, Localization, and Technical Success to resolve complex technical issues while adapting our global support and success frameworks to regional expectations. This is a hybrid Customer Success and Enterprise Support role. You will own a book of Japan-headquartered Enterprise accounts end to end — onboarding, adoption, retention, and growth — while also serving as the senior technical escalation point for APAC support. Responsibilities Customer Success & Growth - Execute the Enterprise customer success strategy in APAC, serving as the primary point of contact for key APAC-based enterprise clients. - Drive initial enablement through high-quality onboarding and training, then define and run a targeted, goal-based engagement plan aligned with each customer's objectives. - Identify and support opportunities for expansion; partner with sales to translate customer insights into actionable growth strategies. - Manage retention by quantifying value delivered, monitoring health, and acting early on risk. - Generate customer insights by understanding the competitive landscape and the end-to-end customer journey — goals, behaviors, and pain points — across distinct APAC markets. - Build durable executive relationships and engage with stakeholders from individual contributors to C-level. - Engage cross-functionally with Product, Engineering, Localization, Sales, and Marketing to drive projects and influence decisions that enable delivery of your customer strategy. Enterprise Support Execution - Work directly with enterprise customers via tickets, Slack, and calls to diagnose and resolve the most complex technical and product questions — the "last line of defense" before Product and Engineering step in. - Own end-to-end troubleshooting: reproduce issues, isolate root causes, collaborate with engineers, and communicate clear, low-jargon explanations in Japanese and English. - Anticipate customer issues, orchestrate timely resolutions, and act as the regional escalation point for complex problems. - Leverage Perplexity's own AI tooling and workflow automations to scale world-class support, continuously replacing repetitive tasks with agentic, self-service solutions that still feel personalized. - Localize 15+ knowledge base articles at launch, maintaining terminology consistency with product UI localization. Qualifications Required - Fluency in English and Japanese is a must; technical writing proficiency in Japanese. Business or fluent-level Korean or Mandarin Chinese is preferred. - 3+ years in B2B/SaaS customer support, ideally as a senior or escalation engineer providing timely, accurate support to end users. - 3+ years in customer-facing roles (Customer Success, Account Management, or equivalent) with a proven ability to engage stakeholders at both project and senior management levels. - Proven track record of driving enterprise-level satisfaction, growth, and retention; creative problem-solving in a fast-paced environment. - Strong customer orientation with the ability to interact effectively with both software engineers and business decision-makers. - Hands-on experience prompting large language models and a solid grasp of AI fundamentals (tokens, context windows, embeddings, latency vs. cost trade-offs). - Ability to translate complex technical concepts — APIs, SSO/SAML, cloud integrations — into clear, actionable guidance for non-technical stakeholders. - Proficiency with modern support platforms (Intercom, Zendesk, Jira) and CRM / customer success tools. - Excellent written and spoken communication; comfortable presenting to C-level executives. - A self-starter comfortable with ambiguity, juggling multiple initiatives, and operating independently as the in-region anchor for a US-headquartered team.

Perplexity is looking for an Applied AI Engineer to design, build, and iterate on cutting-edge agents powering our core experience in Perplexity Computer. Working in this mission critical team, you will develop frontier context layer applications - fulfilling the curiosity of millions of users across the globe. Key Responsibilities - Apply state-of-the-art ML and LLM techniques to solve problems spanning: - Personalization (LLM memory, context summarization, retrieval and ranking); - Contextual recommendations and Monetization applications - Build frontier agent capabilities on top of Perplexity Computer - Build auto research harness for both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact. - Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement and build autonomous harness for agent squad to explore different problem spaces. - Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements. - Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle. Preferred Qualifications - 5+ years experience building and shipping robust AI products for large-scale, user-facing or data-driven products. - Strong software engineering skills (Python, production-quality codebases, collaborative development) and experience using agentic coding tools for large scale parallel developments. - In-depth experience with the full AI lifecycle: data analysis, rigorous evaluation, and ongoing monitoring/improvement. - Proven collaborator and communicator; excels in high-velocity, cross-functional teams. - Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI. - BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience). Bonus Points For - Experience with LLM context engineering or harness engineering. - Experience in mid-training or post-training frontier open source models - Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc).

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and our specialized data sources. We aim to use the latest models as they are released, but the intelligence frontier is a jagged one, and popular benchmarks do not effectively cover our use cases. In this role, you will build specialized evals to improve answer quality across Perplexity, covering search-based LLM answers and other scenarios popular with our users. RESPONSIBILITIES - Architect and maintain automated evaluation pipelines to assess answer quality across Perplexity's products, ensuring high standards for accuracy and helpfulness - Design evaluation sets and methods specifically to measure the impact of tool calls (particularly web search retrieval) on the final answer's quality - Develop VLM-based solutions to programmatically evaluate how final answers render visually across different platforms and devices - Continuously review public benchmarks and academic evaluations for their applicability to the Perplexity product, adapting and incorporating them into our regular performance measurements - Operate within a small, high-impact team where your evaluation metrics directly shape product changes, collaborating closely with technical leadership to measure and improve Answer Quality QUALIFICATIONS - PhD or MS in a technical field or equivalent experience - 4+ years of experience in data science or machine learning - Strong proficiency in Python and SQL (expected to write production-grade code) - Experience building within a modern cloud data stack, specifically AWS and Databricks - Comfortable with agentic coding workflows and using AI-assisted development tools to iterate faster PREFERRED QUALIFICATIONS - 1+ years of experience working with LLMs at scale, specifically with LLM-as-a-judge setups - Prior experience working on customer-facing web products or consumer apps, with real user traffic at scale - A strong research background, with experience applying research methods to real-world ML problems - Experience defining evaluation metrics (e.g., factual consistency, hallucination rate, retrieval precision) and building ground truth datasets

We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL - and we need another engineer to join us. WHAT YOU WILL WORK ON Examples of real work the team does: - New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. - GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. - Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. - Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving. - Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents. WHO WE'RE LOOKING FOR - Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. - You understand modern LLM architectures and are able to bring them up reliably in a production environment. - You've built and operated production distributed systems under real load - ideally performance-critical ones. - Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels. - You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. - Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. GOOD IF YOU TOUCHED ANY OF - ML compilers and framework internals: PyTorch internals, torch.compile, custom operators. - Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism. - Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving. - Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis. - Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads. QUALIFICATIONS - 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. - Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). - Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). - Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).

Perplexity is seeking a TLM (Tech Lead Manager) to lead and grow our highly driven Agents engineering team. The Agents team consists of AI/ML, backend, and full-stack engineers who collaborate to build delightful agentic experiences within our Comet ecosystem https://www.perplexity.ai/comet. Our vision is to empower our users with AI agents that can faithfully actualize their intent, however and wherever expressed, through open-ended interactions with the world. As the Agents TLM, you will bring AI expertise, sharp product intuition, and strong engineering management skills to advance the frontier of what agents can accomplish for our millions of devoted users. You will lead, grow, and support a team in solving many open problems in AI, including: - Designing AI agents to navigate the digital world and perform increasingly valuable units of work for our users; - Training action and decision models that determine, based on complex multimodal states, how to accomplish user-specified objectives; - Providing consistently excellent experiences across desktop, mobile, headless cloud, and other environments through flexible abstractions and frictionless backgrounding; - Developing permission architectures, payload classifiers, and other methods to implement secure-by-design agentic capabilities; - Designing optimal data representations and modes of interaction between agents and their environments; - and much, much more. RESPONSIBILITIES - Provide technical leadership across multiple layers of a rapidly growing product in the AI agents space. - Develop and leverage cutting-edge AI models, infrastructure, and browser technologies to advance the capability frontier and scale those capabilities for a rapidly growing userbase. - Exercise sharp technical & product intuition to guide the team’s system architectures and product roadmaps. - Drive product reliability, code quality, AI evaluation, testing, and maintenance for the broader team. - Oversee hiring, onboarding, and mentorship for a rapidly growing team. Develop rigorous interview pipelines and work closely with recruiting to source candidates. - Interface with the Perplexity co-founders to deliver strategic objectives that redefine what’s possible in the AI industry. QUALIFICATIONS - Strong foundational familiarity with the full AI product stack. - Proficiency in Python (bonus points for TypeScript, Go, and/or Rust). - Domain expertise in at least one of the following areas: - Context engineering and tool interfaces for frontier AI models - Post-training and reinforcement learning (particularly for multimodal models) - Browser technologies (CDP, Playwright, extension development, etc.) - Strong product intuition and taste for user experience excellence. - Strong background and hands-on technical experience with frontier models (the more relevant to open-world agents, the better). - Strong organizational skills for managing and delivering parallel technical projects; ability to guide highly-opinionated teams in making sound tradeoffs and prioritization decisions is critical. - Experience managing engineering teams, including recruiting, growing, and retaining high-caliber talent. - 8+ years of engineering experience, with at least 3 of those years as an engineering manager.

Perplexity is seeking energetic engineers to join our highly driven Agents engineering team. The Agents team consists of backend, full-stack, and AI/ML engineers who collaborate to build delightful agentic experiences within our Comet ecosystem https://www.perplexity.ai/comet and Perplexity Computer https://www.perplexity.ai/computer, our platform for generalized frontier intelligence. Our vision is to empower our users with AI agents that can faithfully actualize their intent, however and wherever expressed, through open-ended interactions with the world. As an engineer on our Agents team, you will bring AI expertise, sharp product intuition, and a tinkerer's mindset to advance the frontier of what agents can accomplish for our millions of devoted users. You will work across applied research and engineering to solve many open problems in AI, including: - Designing AI agents to navigate the digital world and perform increasingly valuable units of work for our users; - Training action and decision models that determine, based on complex multimodal states, how to accomplish user-specified objectives; - Providing consistently excellent experiences across desktop, mobile, headless cloud, and other environments through flexible abstractions and frictionless backgrounding; - Developing permission architectures, payload classifiers, and other methods to implement secure-by-design agentic capabilities; - Designing optimal data representations and modes of interaction between agents and their environments; - and much, much more. RESPONSIBILITIES - Drive cutting-edge AI capabilities across multiple layers of a rapidly growing product in the AI agents space. - Develop and leverage cutting-edge AI models, infrastructure, and browser technologies to advance the capability frontier and scale those capabilities for a rapidly growing userbase. - Ensure a high craft and quality bar, in both AI agent performance and user experience. - Collaborate with fellow engineers, designers, product managers, data scientists, and others across the company to integrate core Perplexity functionality into our frontier agentic products and vice-versa. - Contribute to product reliability, code quality, AI evaluation, testing, and maintenance across the broader team. QUALIFICATIONS - Strong foundational familiarity with the full AI product stack. - Proficiency in Python (bonus points for TypeScript, Go, and/or Rust). - Significant experience in at least one of the following areas: - Context engineering and tool interfaces for frontier AI models - Post-training and reinforcement learning (particularly for multimodal models) - Browser technologies (CDP, Playwright, extension development, etc.) - Strong product intuition and taste for user experience excellence. - Comfortable working with a small, fast-moving team, must be willing to dive in and take ownership. - A passion for shipping products that surprise and delight.

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters RESPONSIBILITIES - Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads - Manage and optimize Slurm-based HPC environments for distributed training of large language models - Develop robust APIs and orchestration systems for both training pipelines and inference services - Implement resource scheduling and job management systems across heterogeneous compute environments - Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure - Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm - Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services - Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands QUALIFICATIONS - Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management - Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization - Experience with deploying and managing distributed training systems at scale - Deep understanding of container orchestration and distributed systems architecture - High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) - Experience managing GPU clusters and optimizing compute resource utilization REQUIRED SKILLS - Expert-level Kubernetes administration and YAML configuration management - Proficiency with Slurm job scheduling, resource management, and cluster configuration - Python and C++ programming with focus on systems and infrastructure automation - Hands-on experience with ML frameworks such as PyTorch in distributed training contexts - Strong understanding of networking, storage, and compute resource management for ML workloads - Experience developing APIs and managing distributed systems for both batch and real-time workloads - Solid debugging and monitoring skills with expertise in observability tools for containerized environments PREFERRED SKILLS - Experience with Kubernetes operators and custom controllers for ML workloads - Advanced Slurm administration including multi-cluster federation and advanced scheduling policies - Familiarity with GPU cluster management and CUDA optimization - Experience with other ML frameworks like TensorFlow or distributed training libraries - Background in HPC environments, parallel computing, and high-performance networking - Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices - Experience with container registries, image optimization, and multi-stage builds for ML workloads REQUIRED EXPERIENCE - Demonstrated experience managing large-scale Kubernetes deployments in production environments - Proven track record with Slurm cluster administration and HPC workload management - Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure - Experience supporting both long-running training jobs and high-availability inference services - Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Perplexity is seeking an experienced Machine Learning Research Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. Responsibilities - Relentlessly push search quality forward — through models, data, tools, or any other leverage available - Architect and build core components of the search platform and model stack - Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models - Conduct advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval - Deploy models — from boosting algorithms to LLMs — in a scalable and performant way - Build and optimize RAG pipelines for grounding and answer generation - Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery Qualifications - Deep understanding of search and retrieval systems, including quality evaluation principles and metrics - Proven track record with large-scale search or recommender systems - Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models - Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications - Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR) - Self-driven, with a strong sense of ownership and execution - Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas

Perplexity is seeking a Research Engineering Manager to lead the team of all-star AI researchers and engineers responsible for developing the models that drive our products. Our team has developed some of the most advanced models for agentic research, query understanding, and other domains that require accuracy and depth. As we expand our userbase and portfolio of product surfaces, our in-house models are increasingly critical to providing a premium, high-taste experience for the world’s most sophisticated users. You will dive into our rich datasets of conversational and agentic queries, leveraging cutting‑edge training techniques to scale AI model performance. Through hands-on technical and organizational leadership, you will empower your team to develop SotA models for the use cases that matter most to our business and our users. RESPONSIBILITIES - Lead a team of researchers and engineers focused on training SotA models for Perplexity-relevant use cases, leveraging the latest supervised and reinforcement learning techniques. - Drive research and engineering efforts to develop production models through advanced model training and alignment techniques, including RL, SFT, and other approaches. - Become deeply familiar with the team’s technical stack, leading from the front through hands-on technical contributions. - Own the data, training, and eval pipelines required to train and continuously improve LLM models. - Design and iterate on model training and finetuning algorithms (e.g., preference‑based methods, reinforcement learning from human or AI feedback) through an approach that balances scientific rigor and iteration velocity. - Design evaluations and improve the production model training pipeline to reliably deliver models that lie on the Pareto frontier of speed and quality. - Work closely with engineering teams to integrate in-house models into our product and rapidly iterate based on real‑world usage. - Manage day‑to‑day execution, project planning, and prioritization for the model training team to hit ambitious quality and performance goals. QUALIFICATIONS - Proven experience with large-scale LLMs and Deep Learning systems. - Strong Python and PyTorch skills; versatility across languages and frameworks is a plus. - Experience leading or managing research or engineering teams working on large-scale AI model development, including driving complex projects from idea to production. - Self‑starter with a willingness to take ownership of tasks and navigate ambiguity in a fast‑moving environment. - Passion for tackling challenging problems in AI model quality, speed, safety, and reliability. - 10+ years of technical experience, with at least 2 of those years as a manager and at least 4 of those years working on large-scale AI model development. NICE-TO-HAVE - PhD in Machine Learning or related areas. - Experience training very large Transformer-based models with techniques such as SFT, DPO, GRPO, RLHF‑style methods, or related preference‑based optimization approaches. - Prior experience designing evaluations and production training pipelines for large‑scale models in a high‑growth environment.

Perplexity is seeking an experienced Machine Learning Research Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. Responsibilities - Relentlessly push search quality forward — through models, data, tools, or any other leverage available - Architect and build core components of the search platform and model stack - Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models - Conduct advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval - Deploy models — from boosting algorithms to LLMs — in a scalable and performant way - Build and optimize RAG pipelines for grounding and answer generation - Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery Qualifications - Deep understanding of search and retrieval systems, including quality evaluation principles and metrics - Proven track record with large-scale search or recommender systems - Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models - Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications - Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR) - Self-driven, with a strong sense of ownership and execution - Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas

Perplexity is looking for a Search Machine Learning Engineer Intern to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. You will work closely with experienced engineers to improve search quality, experiment with new models, and ship features that directly impact how users search and discover information. Internship program: 12 - 24 weeks, full-time, in-person in the Belgrade office. Responsibilities: - Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers. - Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models. - Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks. - Support deployment and monitoring of search and ranking models in a scalable and performant way. - Help build and iterate on RAG pipelines for grounding and answer generation. - Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML. Qualifications: - Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems. - Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects. - Familiarity with evaluating model quality using offline metrics and/or A/B testing is a plus, but not required. - Previous experience (internships, research, or significant projects) working on search, recommendation, or NLP is a plus, but not required. - Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment - Experience with Rust will be a plus

Perplexity is seeking top-tier AI Research Scientists and Engineers to advance our AI products and capabilities. We're building the future of AI-powered search and agent experiences through our Sonar models, Deep Research Agent, Comet Agent, and Search products. Join us in creating SOTA experiences that handle hundreds of millions of queries and continue to scale rapidly. Team Structure Depending on your interests and expertise, you'll work on one of three specialized teams: 1. Core Research Team (Horizontal) Focus on generating and improving base models that power all our products. This team works on foundational model capabilities, post-training techniques, building RL infra and infrastructure that benefits the entire organization. 2. Agent Products Team (Vertical) Concentrate on fine-tuning and optimizing models for our Deep Research Agent and Labs/Canvas products. This team bridges research and product, ensuring our agent capabilities deliver exceptional user experiences. 3. Comet Agent Team (Vertical) Dedicated to developing and enhancing our Comet Agent product. This specialized team focuses on the unique requirements and optimizations needed for Comet's specific use cases. Responsibilities Research & Development - Post-train SOTA LLMs using the latest supervised and reinforcement learning techniques (SFT/DPO/GRPO) - Leverage our rich query/answer dataset to scale model performance across Sonar, Deep Research, Comet, and Search products - Stay current with the latest LLM research, especially in model training, optimization, and personalization techniques - Implement preference optimization and personalization capabilities to enhance user experience - Invent in-house improvements and optimizations to enhance SOTA models - Turn research ideas into algorithms and run experiments to launch new models Infrastructure & Implementation - Own full-stack data, training, and evaluation pipelines required for model development - Build robust and effective training frameworks (on top of Megatron/PyTorch) for post-training LLMs - Implement necessary infrastructure and components to support cutting-edge model training at scale - Integrate models seamlessly into our product ecosystem Collaboration - Work closely with engineering teams to integrate models into Perplexity's product suite - Collaborate across teams to ensure cohesive AI experiences throughout our platform - Partner with product teams to understand user needs and translate them into model improvements Qualifications Required - Proven experience with large-scale LLMs and Deep Learning systems - Strong programming skills in Python/PyTorch; versatility is a plus - Experience with post-training techniques and reinforcement learning - Self-starter with a willingness to take ownership of tasks - Passion for tackling challenging problems - Minimum 2-6 years of experience on relevant projects (depending on seniority level) Nice-to-have - PhD in Machine Learning, AI, Systems, or related areas - Experience in post-training LLMs with SFT/DPO/GRPO - C++/CUDA programming skills - Experience building LLM training frameworks - Academic publications and research impact - Experience with agent systems and multi-step reasoning - Background in personalization and preference learning

Internship Program Berlin Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office. Responsibilities - Relentlessly push search quality forward — through models, data, tools, or any other leverage available. - Train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models. - Conduct research in representation learning, including contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval. - Build and optimize RAG pipelines for grounding and answer generation. Qualifications - Understanding of search and retrieval systems, including quality evaluation principles and metrics. - Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models. - Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation. - Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).

Perplexity is seeking an exceptional AI Research Tech Lead to drive our research strategy and lead the development of our in-house Online LLMs, the Sonar models. In this leadership role, you will set the macro research direction across different modalities, mentor a team of researchers, and take advantage of our rich query/answer dataset to continue scaling our Sonar model performance and deliver the SOTA Online LLM experience to our users. RESPONSIBILITIES RESEARCH LEADERSHIP & STRATEGY - Define and execute the macro research direction across multiple modalities, including post-training LLMs for agent trajectories and future mid-training initiatives - Lead strategic research planning and roadmap development to advance Sonar model capabilities - Drive innovation in supervised and reinforcement learning techniques for query answering - Collaborate with leadership to align research priorities with product and business objectives TEAM DEVELOPMENT & MENTORSHIP - Coach and mentor a team of AI research scientists and engineers, fostering their technical and professional growth - Establish the long-term macro research direction across the team, including our direction across different modalities - Lead hiring and onboarding of new research talent - Create a collaborative environment that encourages knowledge sharing and innovation TECHNICAL EXCELLENCE - Post-train SOTA LLMs on query answering using cutting-edge supervised and reinforcement learning techniques - Own and optimize the full stack data, training, and evaluation pipelines required for LLM post-training - Deliver Sonar models that provide SOTA query answering performance - Drive research into agent trajectories and multi-modal capabilities - Lead the technical roadmap for eventual mid-training investments CROSS-FUNCTIONAL COLLABORATION - Work closely with engineering teams to integrate Sonar models into our product - Partner with product teams to understand user needs and translate them into research priorities - Collaborate with data teams to leverage our unique query/answer dataset effectively - Communicate research progress and findings to stakeholders across the organization QUALIFICATIONS REQUIRED - Minimum of 5 years of experience working on relevant AI/ML projects with 3+ years in a technical leadership role - Proven track record of leading and mentoring technical and research teams - A Computer Science graduate degree at a premier academic intitution - Deep expertise with large-scale LLMs and Deep Learning systems - Strong programming skills with versatility across multiple languages and frameworks - Demonstrated ability to set technical vision and drive execution - Experience with pre-training and post-training techniques (self-supervised learning along with SFT/DPO/GRPO/PPO) - Self-starter with exceptional ownership mentality and ability to work in ambiguous environments - Passion for solving challenging problems and pushing the boundaries of AI research NICE-TO-HAVE - PhD in Machine Learning, Computer Science, or related areas - Experience with agent-based AI systems and multi-modal model development - Background in mid-training or pre-training of large language models - Publications in top-tier AI/ML conferences - Experience in fast-paced startup environments - Track record of translating research into production systems