Forward Deployed Engineer - ML
Application analysis
Forward Deployed Engineer - ML at Modal is a forward deployed enterprise software role for engineers who want customer-facing technical work with real deployment ownership.
Reading this role
This Forward Deployed Engineer role centers on embedded customer delivery — discovery, prototyping, integration, and production rollout.
About Modal
Enterprise software forward deployed engineers drive adoption, integration, and measurable customer outcomes. The role is anchored in Stockholm, so expect on-site customer work in that region.
Stand out for Forward Deployed Engineer - ML
- Lead your application with one paragraph tying Forward Deployed Engineer - ML to a specific customer or deployment you owned — Modal hires for shipped outcomes, not tool lists.
- Prepare a 5-minute “week one on site” plan: who you would meet, what you would inspect in their stack, and what artifact you would deliver by day five.
- Show you can go from discovery notes → working integration → production checklist without needing a large internal team.
- This role is tied to Stockholm — mention willingness to work on-site with customers and any existing ties to that market (clients, partners, or domain knowledge).
Stand out at Modal
- Demonstrate stakeholder management with a concrete example — Modal will probe for specifics, not buzzwords.
- Demonstrate integration skill with a concrete example — Modal will probe for specifics, not buzzwords.
- Prepare stories that move from discovery → pilot → production with named outcomes.
- Read recent engineering blog posts, product launches, and customer stories on modal.com — reference one explicitly in your outreach to show genuine interest.
Tailored from this listing’s title, company, and description. Always verify requirements on the company careers page before applying.
Role description
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 Stockholm