Research Scientist / Engineer – Reinforcement Learning Infrastructure

What You'll Own

• Design, build, and scale distributed RL post-training systems, orchestrating trainer, rollout, environment, and reward workloads across thousands of GPUs.

• Build high-throughput rollout generation, integrating inference engines (vLLM, SGLang), weight synchronization, and asynchronous/off-policy schemes.

• Design RL environments for agentic, multi-step tasks — sandboxed code execution, tool use, computer use, multimodal interaction — reproducible and scalable to millions of episodes.

• Build reward infrastructure: verifiable/programmatic rewards, reward-model serving, LLM-as-judge pipelines, and defenses against reward hacking.

• Develop the evaluation, monitoring, and debugging tooling that keeps large RL runs stable.

• Advance training efficiency and stability, and turn new post-training ideas into production runs with researchers.
  

First 90 Days

One way the first 90 could unfold.

• Days 1–30 — Immerse & Diagnose: Learn the current RL stack and where throughput, stability, or correctness break.

• Days 30–60 — Ship & Validate: Improve a piece of the loop (rollout throughput, reward infra, or an environment) and prove it on a real run.

• Days 60–90 — Scale & Systemize: Harden the full loop across thousands of GPUs and asynchronous architectures.
  

What You Bring

• 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.
  

Nice to Have

• 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.
  

About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.