The opportunity
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.
What you'll do
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 have 2+ 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
- 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 London
