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. 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 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 (YC W25) #J-18808-Ljbffr SupportFinity™
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