Forward Deployed Engineer - ML

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’ll help 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.

WHAT YOU’LL WORK ON:

- Work hands-on with companies like Suno, 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 proofs of concept that make Modal’s performance advantages tangible

YOU MAY BE A FIT IF:

- You have 2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure

- You’re familiar with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains and can go deep on at least one (you don’t need all of them)

- You’re a strong communicator who can go deep on technical architecture with an engineering team and clearly articulate tradeoffs to technical leadership

- You have a genuine interest in working directly with customers: you find it energizing to understand someone else’s problem and help them solve it

- You’re willing to work in person in New York City, San Francisco, or Stockholm

NICE-TO-HAVE:

- Side projects, open-source contributions, or published work you’re proud of in ML or systems performance