lumaai - Research Scientist / Engineer – Reinforcement Learning Infrastructure

• Hands-on experience post-training LLMs with RL (PPO/GRPO-family, RLHF, RLVR) at meaningful scale. • Extensive distributed PyTorch training and parallelism (FSDP, Tensor/Pipeline/Expert Parallel) for foundation models. • Experience building RL environments, reward functions, verifiers, or evaluation harnesses for LLM agents, including sandboxed execution and multi-turn tool use. • Deep familiarity with RL post-training frameworks (veRL, OpenRLHF, TRL, Ray orchestration) and rollout inference engines (vLLM, SGLang). • Strong understanding of GPU clusters, networking, and communication libraries (NCCL, MPI) under mixed training and inference workloads. • Running RL training across 100+ GPUs, including asynchronous or disaggregated trainer/rollout architectures. • Containerization and orchestration (Kubernetes, Ray) for large environment fleets and sandboxed workloads. • Research contributions in RL for LLMs, or open-source contributions to RL training frameworks.