Redwood City, California, United States · Hybrid
You'll build the systems that make reinforcement learning work at frontier scale — coupling policy optimization with large fleets of inference workers, agentic environments, and the reward and verification systems that t…
Skills: Reinforcement Learning, Distributed Systems, PyTorch, LLM Post-training, PPO
Redwood City, California, United States · Hybrid
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Redwood City, California, United States · Hybrid
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Los Angeles, California, United States · Remote OK
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San Leandro, California, United States · On-site
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Sunnyvale, California, United States · On-site
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Atascadero, California, United States · On-site
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Brisbane, California, United States · On-site
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Redwood City, California, United States · On-site
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San Francisco, California, United States · Hybrid
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Livermore, California, United States · Hybrid
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Palo Alto, California, United States · On-site
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San Diego, California, United States · On-site
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San Carlos, California, United States · On-site
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Redwood City, California, United States • Hybrid
Sign up with Clera and we'll reach out the moment a role actually fits you — no more spraying applications into the void.
You will design, build, and scale distributed reinforcement learning infrastructure to orchestrate training, rollout, and reward workloads across thousands of GPUs. Additionally, you will develop evaluation and monitoring tools to ensure the stability and efficiency of large-scale RL post-training pipelines.
Candidates must have hands-on experience with RL post-training for LLMs at scale and deep familiarity with distributed PyTorch training and parallelism. Proficiency in building RL environments, reward functions, and managing asynchronous rollout pipelines is essential for this role.
You'll build the systems that make reinforcement learning work at frontier scale — coupling policy optimization with large fleets of inference workers, agentic environments, and the reward and verification systems that turn model behavior into learning signal. RL is how Luma's models go from capable to useful.
RL at scale is a full-loop systems problem: training, rollout generation, environment execution, and reward computation running concurrently across thousands of GPUs, all needing to stay fast, stable, and correct together. It fits someone who has lived this — post-trained LLMs with RL, built environments and verifiers, and debugged asynchronous rollout pipelines at scale. If you haven't operated RL at real scale, this will be deep water.
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.
Luma AI’s mission is to build Multimodal AGI: AI that can generate, understand, and operate in the physical world. We develop multimodal models across video, 3D, and generative media, and ship them in products like Dream Machine to help creators and teams turn ideas into compelling visuals—fast.
Offices: San Francisco Bay Area, CA, US
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