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Osmosis (YC W25)
Machine Learning Engineer

Machine Learning Engineer

On site

San francisco, United states

$ 250,000 /year

Full Time

13-01-2026

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Skills

Python TypeScript Docker Kubernetes Resource Allocation Training Machine Learning PyTorch Databases react AWS Next.js FastAPI

Job Specifications

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

About the Company

Osmosis is a reinforcement fine-tuning platform that helps companies create task-specific models that outperform foundation models at a fraction of the cost. Know more