Research Scientist / Engineer – Reinforcement Learning Infrastructure

AItoolnavio; France; À distance; EUR 120 000 - 190 000; Plein temps

Résumé du poste

Luma is seeking an expert to design, build, and scale distributed RL post-training systems for large-scale LLMs. You will orchestrate trainer, rollout, environment, and reward workloads across thousands of GPUs, with high-throughput rollout generation and asynchronous weight synchronization.

You will develop RL environments, reward infrastructure, and verifiers, plus evaluation tooling to keep runs stable. This role emphasizes scaling efficiency and rapid productionization with researchers.

Qualifications

• Hands-on experience post-training LLMs with RL at scale (PPO/GRPO/RLHF).
• Extensive distributed PyTorch training for foundation models.
• Experience building RL environments, reward functions, verifiers, or evaluation harnesses.
• Familiarity with RL post-training frameworks (veRL/OpenRLHF/TRL) and rollout engines (vLLM/SGLang).
• Understanding of GPU clusters, networking, and libraries (NCCL, MPI).

Responsabilités

• Design, build, and scale distributed RL post-training systems across thousands of GPUs.
• Build high-throughput rollout generation with inference engines and async weight syncing.
• Create RL environments for multi-step tasks with sandboxed execution and tool use.
• Develop verifiable rewards, LLM-as-judge pipelines, and defenses against reward hacking.
• Develop evaluation, monitoring, and debugging tooling for large RL runs.
• Turn new post-training ideas into production with researchers.

Connaissances

RL with LLMs

Distributed PyTorch training

RL environments & evaluators

RL frameworks

Rollout inference engines

GPU clusters & tooling

Outils

Kubernetes

Ray

NCCL

MPI

vLLM

SGLang

Description du poste

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.

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.