Machine Learning Engineer (Reinforcement Learning)

We are seeking a Machine Learning Engineer to develop and scale distributed reinforcement learning systems for model training. This role involves deploying elastic environment microservices, designing effective reward systems, and optimizing multi-node and multi-datacenter training pipelines to enhance performance and efficiency.

Must Have

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

Good to Have

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

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