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