The opportunity
AI needs a new infrastructure layer. We're building it at Modal.
What you'll do
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
+ years of professional ML engineering experience, ideally with hands-on work: in inference optimization, model training, GPU programming, or ML infrastructure
What they're looking for
- 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
