Modal은 Forward Deployed ML Engineer를 채용합니다. 고객사의 LLM 서빙 및 모델 학습 워크로드를 최적화하는 핵심 역할을 수행합니다. 2년 이상의 ML 엔지니어링 경력과 추론/학습 툴체인에 대한 깊은 이해가 필수입니다. 기술적 전문성과 고객 지향적 커뮤니케이션 능력을 갖춘 분을 찾습니다. 최첨단 AI 인프라를 구축하는 팀에서 직접적인 기술적 임팩트를 경험하세요.
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, Ramp, 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 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,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
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
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
