RLlib

RLlib is the industry-standard open-source library for reinforcement learning, built on top of Ray. As technical owner at Anyscale, I lead stability, performance, and correctness across RLlib’s distributed training and inference stack.

Key contributions:

• Diagnosing and eliminating high-impact failure modes (hangs, deadlocks, non-determinism, resource leaks) in large-scale RL workloads
• Benchmark-driven performance engineering and regression-prevention guardrails
• CI stress tests, determinism checks, and performance gates at scale

Stack: Python, Ray, PyTorch, Kubernetes, distributed systems