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.)
