Machine Learning Engineer (Reinforcement Learning)

We’re looking for an MLE to build and scale distributed reinforcement learning systems for model training. You’ll deploy elastic environment microservices, design reward systems and optimize multi-node and multi-datacenter training pipelines.

Responsibilities:

• Designing and implementing RL pipelines from reward modeling to policy optimization
• Optimizing RL training stability and sample efficiency for large models
• Verifying numerical correctness across inference and training
• Performance engineering on trainer-inference communication
• Validating methods from recent publications

Qualifications:

• Hands-on experience with reinforcement learning in production systems
• Deep understanding of policy-space methods (GRPO, PPO, etc.)
• Experience profiling distributed systems

Preferred:

• History of OSS contributions
• Knowledge of TorchTitan and SGLang or vLLM