Modal

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal is the cloud platform built for AI. We're used by the world's leading AI labs, startups, and researchers to run compute-intensive workloads: training runs, inference, sandboxed code execution, and more. We need someone to own and grow our presence in academia and research. That means running our Modal for Academics program https://modal.com/academics: sponsoring courses and labs with compute credits, building relationships with professors and researchers at top institutions, and making Modal the default choice when someone needs GPUs for their next paper. The right person has lived in this world. You know how grant cycles work, how labs are structured, and how the conference publishing process actually runs. You've also shown you can operate beyond the lab, whether that's organizing events, building community, or working across institutional boundaries. In this role, you will: - Own and operate the Modal for Academics program end-to-end: handling inbound inquiries, overseeing grant decisions, onboarding, and follow-through. - Identify and pursue sponsorship opportunities with university courses, ML research labs, and academic conferences, with a bias toward work that's likely to be widely read and cited. - Build relationships with professors and researchers at top universities. - Partner with leading AI research labs on compute grants and collaborative programs. - Track outcomes: which grants produced papers, citations, talks, or downstream Modal adoption. - Represent Modal at academic conferences (NeurIPS, ICLR, ICML, and others) and research-adjacent events. - Develop outreach templates, program documentation, and supporting content to scale inbound interest. REQUIREMENTS: - A PhD or research Master's from a strong program in ML/CS or with a large computational component. You know how grants work, how labs are structured, and how the conference process actually runs. - Evidence that you've operated beyond pure research: organizing a workshop, running a student group, working with a grants office, industry internships, or similar. - Strong written and verbal communication, including on technical matters. You'll be representing Modal to professors and researchers at top universities. - Good judgment about what's worth pursuing. Not every course sponsorship or lab partnership is equal. - Organized and self-directed. This role spans dozens of active relationships at any given time. - Comfortable working in-person in a fast-paced startup environment. Nice to have: - Experience as a conference organizer, area chair, workshop chair, or reviewer at a major ML conference. - Existing relationships with professors and researchers at top universities.

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We're looking for strong backend engineers who love building a developer tools used by the largest AI companies in the world. You’ll be building for things at scale, but also for new AI workflows that change every day. REQUIREMENTS: - Experience building and shipping modern web applications end-to-end. We care more about what you’ve built than how many years you’ve been building. - Comfort working across the stack: TypeScript on the frontend, Python services on the backend, and ClickHouse for data and analytics. - Deep knowledge of observability tools and patterns used for large-scale workloads such as custom sandboxes, training and inference for large language (LLM) and diffusion models. - Experience with at least one of: billing/payments systems, B2B SaaS tooling, or enterprise software, or LLM / diffusion models inference and training loads. - Strong product instincts; you think about customer problems, not just tickets. - Ability to participate in on-call rotation and respond to production incidents. - Ability to make good tradeoffs between shipping fast and building for scale. - Ability to work in-person in our NYC or SF office.

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We are looking for strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you! REQUIREMENTS: - 5+ years of experience writing high-quality, high-performance code. - Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT). - Familiarity with Nvidia GPU architecture and CUDA. - Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc). - Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal builds AI infrastructure products that developers love. That's how we grew so quickly and why word-of-mouth remains one of our most important channels today. From powering one of the largest vibe-coding platforms at Lovable https://modal.com/blog/lovable-case-study to enabling teams like Ramp to build their own internal coding agents https://builders.ramp.com/post/why-we-built-our-background-agent, Modal Sandboxes are used by developers to safely execute AI-generated code at scale. We're now hiring our first developer relations engineer focused on Modal Sandboxes. Whether it’s banger tweets https://x.com/charles_irl/status/1950377313822265565, in-depth technical resources https://modal.com/gpu-glossary or long-form talks https://www.youtube.com/watch?v=y-UGrYbJsJk, we want to meet developers by any medium necessary and empower them to build and ship novel AI products. In this role, you will primarily be creating and distributing technical content that is unique, educational, and practical. This content will be the first Modal touchpoint for many of our users. We want to not only showcase the power and developer experience of Modal, but also be a trusted resource for them when it comes to implementing new AI technologies. In this role, you will: - Ship high quality technical content (videos, cookbooks, integrations, creative mini-apps) that teaches developers how to use Modal Sandboxes for LLM-powered code execution, vibe-coding apps, RL environments and beyond. - Distill the latest advancements in AI technology and educate developers on how to incorporate them. - Give demos/talks about Modal and adjacent tools at developer events. - Engage with users in our community, both online (X, LinkedIn, Slack) and at in-person events. - Build relationships, integrations, and joint marketing activities with other developer-focused companies - Set objectives that are aligned with the greater GTM team and track the impact of the initiatives you work on. REQUIREMENTS: We are looking for someone who: - Has built something that rhymes with a vibe-coding platform, hosted background agents, or other AI systems that execute generated code in isolated environments. - 3+ years as a software engineer and at least 1 year experience using ML, LLMs, or agentic systems. - Is energized by the AI developer community and wants to help developers adopt new technologies. - Loves teaching. - Has excellent technical communication skills. - Is metrics-driven and takes quantitative approaches to prioritizing initiatives. - Is excited about working in-person in the NYC, SF or Stockholm office. - Bonus: you're not afraid to think outside the box when it comes to compelling technical content. - Bonus: you already have a developer following on social media!

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We're looking for engineers with deep AI/ML and low-level systems experience who want to build the best technical support experience in the world. This isn't a traditional support role — it's an engineering role where you happen to be closest to our customers. You'll split your time roughly 50/50 between working directly with customers and shipping fixes, features, and automation that improve Modal for everyone. When you help a customer debug a training run, you'll also fix the underlying issue in the platform. When you notice ten customers hitting the same friction point, you'll build the tooling or automation that eliminates it entirely. This role is for people who solve problems, not people who answer tickets. The problems you encounter are deeply technical and arise from running some of the most demanding AI workloads in the world. You'll be a member of our engineering team, contributing production code alongside the engineers building the core platform. The difference is that your roadmap is shaped by what you learn at the frontier of customer experience. You will: - Ship code that matters. Fix bugs, build features, and create automation that improves the experience for every Modal user — not just the one who reported the issue. - Work directly with customers. Help developers and ML engineers debug, optimize, and architect their workloads across Slack, email, and calls. - Build scalable systems. Design tooling, dashboards, and automated workflows that make support efficient at scale — delighting customers at the most important moments. - Close the feedback loop. Translate patterns you see in the field into concrete improvements — docs fixes, API changes, or new feature proposals. - Contribute to open source and technical content. Write examples, build demos, and publish content that helps the broader community succeed on Modal. REQUIREMENTS: - Accomplished in key areas. You bring depth in either low-level infrastructure or ML/AI, and you're not lost in the other. - Low-level infrastructure experience. Operating systems, file systems, networking, performance profiling, cluster management and distributed systems. - AI/ML engineering experience. Training models, optimizing inference, working with GPUs, or building ML infrastructure. - Automation mindset. Your instinct when you see a manual process is to eliminate it and you have the engineering background to make that happen. - Clear communicator. Can explain a systems issue to a customer, write a crisp bug report, and draft documentation, all while collaborating internally to ship improvements.

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We’re hiring an Enterprise Account Executive to accelerate Modal’s growth with the world’s most innovative AI companies. This is a high-impact role where you’ll own the full sales cycle—from building pipeline to closing large, strategic enterprise deals. You’ll partner directly with our founders, engineering, and product teams to help customers harness Modal’s infrastructure to train, deploy, and scale AI applications. You’ll be expected to operate as a builder: developing new relationships, shaping our GTM motion, and serving as the voice of the customer inside Modal. The ideal candidate is both technically curious and commercially driven—equally comfortable in a room with C-level executives and with machine learning engineers. In this role, you will: - Drive new business by generating pipeline, negotiating, and closing complex enterprise deals - Build deep, trusted relationships with technical and executive stakeholders at leading AI companies - Run proof-of-concepts and pilots in close collaboration with our solutions and engineering teams - Expand existing accounts by identifying growth opportunities and helping customers scale their workloads - Bring structured customer feedback to influence Modal’s product roadmap and GTM strategy - Contribute to the foundation of Modal’s sales process, culture, and playbooks as we scale the team REQUIREMENTS: - 7–10+ years of enterprise software sales experience, ideally in infrastructure, ML/AI, or developer-focused platforms - Track record of consistently exceeding $1M+ annual quotas and closing six- and seven-figure enterprise deals - Strong technical acumen—able to communicate infrastructure and AI concepts with both engineers and executives - Experience leading proof-of-concepts and managing complex procurement processes - Excellent communication, negotiation, and relationship-building skills - Comfortable in an early-stage, fast-moving environment; excited to help shape systems and processes from the ground up - Ability to work in-person from our office 5 days a week

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We're looking for Forward Deployed ML Engineers who want to work at the intersection of deep technical work and direct customer impact. As an ML FDE, you'll partner with leading AI companies and foundation model labs to help them achieve state-of-the-art performance on their most demanding workloads — LLM serving, model training (SFT, RLHF), audio pipelines, scientific computing, and more. You're helping teams reach outcomes most engineers can't on their own. The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the AI stack, and energy for working directly with customers on hard problems. You will: - Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal - Contribute to open-source projects — members of the team are active contributors to SGLang — and publish technical content that demonstrates Modal's capabilities across the AI stack - Collaborate with Modal's product and sales teams, contributing to the platform as both an engineer and a product stakeholder - Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work - Conduct technical demos, experiments, and proof-of-concepts that make Modal's performance advantages tangible REQUIREMENTS: - 2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure - Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains. You don't need all of these, but you should be able to go deep on at least one. - Strong communicator who can go deep on technical architecture with an engineering team and clearly articulate tradeoffs to technical leadership - Genuine interest in working directly with customers — you find it energizing to understand someone else's problem and help them solve it - Bonus: side projects, open-source contributions, or published work you're proud of in ML or systems performance - Willing to work in-person in New York City, San Francisco, or Stockholm

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal is the cloud platform built for AI. We're used by the world's leading AI labs, startups, and researchers to run compute-intensive workloads: training runs, inference, sandboxed code execution, and more. We're hiring a Community Manager in SF to make Modal a fixture in the AI developer community. You'll bring developers together through meetups, hackathons, and events of our own, and build the kind of community that keeps showing up. You know how to rinse and repeat the process, but always with a creative bend. In this role, you will: - Co-host developer meetups with partners in our ecosystem. Find the right speakers, build the relationships, and run the events together. Prior examples: High Performance Inference for Open LLMs https://luma.com/52oxja09, Voice AI Builders Night https://luma.com/voice_builders, RL with Modal and Prime Intellect https://luma.com/5idpy60y, FDE Happy Hour https://luma.com/modal-q9w2. - Sponsor hackathons that attract highly technical engineers and researchers in ML and AI Infra. For example: Autoresearch Hackathon with OpenAI https://luma.com/fvz1h1dq, TreeHacks, HackIllinois. - Own and grow the webinar program: Partner with Modal engineers and FDEs to identify topics, speakers, promotion, and turning attendance into funnel growth. - Run creative events that break the standard. For example: eBPF documentary screening https://luma.com/komtsou0, Modal Art Show with Gray Area, etc. - Help community members run their own Modal events. Build the playbooks, templates, and support that let other people host meetups under the Modal banner, so events scale past what one person can run. - Turn events into content and campaigns. Work with Marketing so every event produces recaps, clips, talks, and stories that reach people who weren't in the room. - Own events end to end: venues, partners, speakers, logistics, promotion, day-of, and follow-up. - Track what's working: attendance, downstream signups and adoption and users within target accounts. Do more of what pulls and cut what doesn't. REQUIREMENTS: - A track record running events or building community. You've organized meetups, conferences, hackathons, or similar, and can point to specific ones that worked. - Plugged into the SF AI and developer scene, or able to get there fast. You know which companies, projects, and people are worth partnering with — and which trends and movements are worth jumping on, inside and outside of tech (e.g. agents, mahjong, board game nights). - Strong written and verbal communication. You'll represent Modal to developers, partners, and speakers, sometimes on stage. - Good judgment about what's worth doing. Not every event or sponsorship is worth the time, and you can tell the difference. - Organized and self-directed. This role runs many events and partner relationships at once. - Comfortable working in person in SF in a fast-paced startup environment. Nice to have: - Existing relationships across the SF AI ecosystem: companies, open-source projects, DevRel teams, other community organizers. - Experience in developer marketing or DevRel. - Experience building a community-led or ambassador program. - Familiarity with Modal or the AI infrastructure space.

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We are looking for strong engineers with experience training production machine learning models. If you are interested in contributing to open-source projects and evolving Modal's infrastructure to train the next generation of language models, we'd love to hear from you! REQUIREMENTS: - 5+ years of experience writing high-quality, high-performance code. - Experience working with torch and high-level training frameworks (Huggingface, verl, slime) - Experience with ML training optimization (tell us a story about eliminating data loading bottlenecks, overlapping communications with compute, rewriting a trainer to handle off-policy rollouts, etc.) - Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc). - Ability to work in-person, in our NYC or San Francisco office. - Ability to participate in on-call rotation and respond to production incidents.

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal is hiring a high-impact Solutions Architect to drive technical strategy across our most strategic enterprise accounts. You will operate as the executive technical counterpart to Enterprise Account Executives, leading complex evaluations, shaping infrastructure modernization roadmaps, and driving multi-product adoption across AI/ML workloads. This role is not demo support. It is a strategic, consultative position requiring strong architectural depth, executive presence, and the ability to influence 7–8 figure infrastructure decisions. You will work directly with CTOs, VPs of Engineering, and ML platform leaders to help them rethink how AI infrastructure should be built and operated. If you thrive in high-velocity technical sales environments and want to shape the infrastructure layer powering modern AI companies, this role is for you. WHAT YOU’LL DO: - Own the technical strategy for large, complex enterprise accounts - Partner with Enterprise Account Executives to drive 6–7+ figure opportunities from qualification through close - Lead deep technical discovery across platform, ML, DevOps, and infrastructure stakeholders - Architect end-to-end migration strategies from AWS, GCP, or Azure to Modal’s serverless infrastructure - Drive executive-level technical conversations that connect infrastructure architecture to business impact (velocity, cost efficiency, reliability) - Design and oversee proof-of-concepts that demonstrate production-grade scalability and performance - Navigate security reviews, compliance discussions, and procurement processes with confidence - Build durable relationships with technical champions and executive sponsors - Orchestrate cross-functional virtual account teams (Sales, Product, Engineering, Support) to ensure successful adoption - Influence Modal’s product roadmap by surfacing structured enterprise feedback - Mentor other Solutions Architects and contribute to technical best practices across the team REQUIREMENTS: - 5+ years in Solutions Engineering, Sales Engineering, or enterprise-facing technical roles in infrastructure, cloud, or developer platforms - Demonstrated success driving complex enterprise sales cycles ($250K+ ACV; experience with $1M+ ARR accounts strongly preferred) - Experience working with large, sophisticated Bay Area technology companies or global enterprises - Deep expertise in cloud infrastructure (AWS, GCP, Azure) including compute, networking, storage, and managed services - Strong architectural understanding of containers and orchestration (Docker, Kubernetes) - Experience designing distributed systems and production data platforms - Strong working knowledge of ML/AI infrastructure (training pipelines, inference systems, GPU workloads, MLOps) - Experience leading cloud migration or infrastructure modernization initiatives - Familiarity with Infrastructure as Code (Terraform, Pulumi, CloudFormation) and CI/CD systems - Production programming experience in Python strongly preferred - Executive-level communication skills with the ability to influence technical and business stakeholders - Proven ability to lead cross-functional virtual teams in high-stakes account environments - Strong business acumen and understanding of enterprise procurement and buying processes - Willingness to travel up to 30%

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal is seeking an experienced Forward Deployed Engineer (FDE) to partner with our sales team and drive technical sales success. As an FDE, you will be the technical voice in our sales process, working directly with Account Executives to help enterprise customers understand how Modal can transform their AI/ML infrastructure. You will: - Partner with Account Executives to identify, qualify, and close strategic enterprise opportunities - Lead technical discovery sessions with prospective customers to understand their current infrastructure, pain points, and requirements - Design and present compelling technical solutions that demonstrate how Modal addresses customer needs - Architect migration paths from existing cloud infrastructure (AWS, GCP, Azure) to Modal's serverless platform - Conduct technical demos, experiments, and proof-of-concepts that showcase Modal's capabilities - Navigate complex technical evaluations and address security, compliance, and integration concerns - Build trusted advisor relationships with technical decision-makers including CTOs, VPs of Engineering, and ML Engineering leads - Collaborate with product and engineering teams to communicate customer feedback and influence product roadmap - Support contract negotiations by providing technical expertise on implementation timelines, resource requirements, and success metrics REQUIREMENTS: - 5+ years of experience in solutions engineering, sales engineering, or customer-facing technical roles - Deep hands-on experience with cloud platforms (AWS, GCP, Azure) including compute, storage, networking, and managed services - Strong knowledge of containerization technologies (Docker, Kubernetes, container orchestration) - Experience with databases (SQL/NoSQL), data pipelines, and distributed systems architecture - Understanding of ML/AI infrastructure challenges including model training, inference, and MLOps workflows - Familiarity with Infrastructure as Code (Terraform, Pulumi, CloudFormation) and CI/CD pipelines - Proven track record of supporting enterprise software sales cycles ($100K+ ACV) - Exceptional presentation and communication skills with ability to explain complex technical concepts to both technical and business audiences - Strong business acumen with understanding of enterprise buying processes and procurement - Experience building migration strategies and implementation roadmaps for large-scale infrastructure changes - Ability to work effectively with cross-functional teams including sales, product, and engineering - Experience selling or implementing serverless computing, container platforms, or ML infrastructure solutions preferred - Willingness to travel up to 30% for customer meetings and industry events - Ability to work in-person in our Stockholm office