Research Scientist / Engineer – Reinforcement Learning Infrastructure

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.

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.

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.

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.