Member of Technical Staff - ML Training Systems
머신러닝 엔지니어정규직리드 · 5년 이상New York, San Francisco출근
PythonPyTorchHuggingfaceML Training Optimization
Modal은 차세대 AI 인프라를 구축하는 ML Training Systems 엔지니어를 찾습니다. PyTorch 및 ML 학습 최적화 경험이 필수이며, 대규모 모델 학습 인프라를 고도화하는 역할을 수행합니다. 5년 이상의 경력을 보유한 숙련된 엔지니어를 우대하며, 뉴욕 또는 샌프란시스코 사무실에서 상주 근무가 가능해야 합니다.
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 are looking for strong engineers with experience training production machine learning models. If you are interested in contributing to open-source projects and evolving Modal's infrastructure to train the next generation of language models, we'd love to hear from you!
5+ years of experience writing high-quality, high-performance code.
Experience working with torch and high-level training frameworks (Huggingface, verl, slime)
Experience with ML training optimization (tell us a story about eliminating data loading bottlenecks, overlapping communications with compute, rewriting a trainer to handle off-policy rollouts, etc.)
Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).
Ability to work in-person, in our NYC or San Francisco office.
Ability to participate in on-call rotation and respond to production incidents.