Machine Learning Systems Engineer, RL Engineering

When to Use

Design RL training platform — controllers, workers, resource scheduling
Implement rollout collection — vectorized envs, async actors, trajectory buffers
Operate distributed training — data parallel, parameter servers, gradient sync patterns
Manage replay buffers — prioritization, storage, sampling at scale
Wire checkpointing — policy/value nets, optimizer state, resume after preemption
Integrate experiment tracking — seeds, configs, metric schemas, artifact lineage
Connect simulators — Gymnasium-style APIs, custom env servers, batch stepping
Export policies for batch eval or downstream inference path
Debug training instability — NaNs, reward scale, worker desync, straggler GPUs
Plan GPU/memory layout for actor vs learner processes