Machine Learning Engineer

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.

• 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

• 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

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).