Machine Learning Engineer

San Francisco

**About Osmosis** At Osmosis, we help companies use cutting-edge reinforcement learning techniques to fine-tune open-source language models that beat foundation models on performance, latency, and cost. We've raised $7M in funding from Y Combinator, top institutional investors like CRV and Audacious Ventures, as well as angel investors including Paul Graham (Y Combinator), Erik Bernhardsson (Modal Labs), Misha Laskin (Reflection AI), and Guillermo Rauch (Vercel). **About The Role** We're looking for a Machine Learning Engineer to contribute to high-performance distributed training infrastructure for RL at scale. You'll work directly with our founding team and design partners to push the boundaries of what's possible with post-training and continual learning systems. This role requires expertise in RL algorithms, distributed training, and low-level optimization. You'll have exceptional agency to make impactful decisions while working in a fast-paced, customer-driven environment. **Responsibilities** You'll contribute to work in areas like: * Distributed Training Infrastructure: implement new RL algorithms and build scalable post-training pipelines * Resource Management \& Optimization: design infrastructure systems for efficient GPU utilization and dynamic resource allocation * Customer-Facing Work: work directly with customers on production deployments and custom model development **Technology** * Backend: Python FastAPI, Golang * Frontend: React, TypeScript, Next.js * Cloud Infrastructure: AWS Fargate, Docker, Kubernetes, AWS SageMaker * ML Frameworks: Verl / slime / Megatron-LM / SkyRL, PyTorch (FSDP experience is a plus), vLLM / SGLang * Databases: DynamoDB, S3