# Research Scientist / Engineer – Reinforcement Learning Infrastructure Source: https://realanalystjobs.com/job/research-scientist-engineer-reinforcement-learning-infrastructure-luma-ai-832af533 Research Scientist / Engineer – Reinforcement Learning Infrastructure at Luma Ai, an on-site role based in London, UK. Posted on 8 October 2026, read from Luma Ai's own careers page (Ashby), which RealAnalystJobs re-checks daily and removes the role when it closes. Updated 15 November 2026. ## Key facts - Company: [Luma Ai](https://realanalystjobs.com/companies/luma-ai) - Location: London, UK - Work mode: On-site - Posted: Oct 8, 2026 - Employment type: Full time - Source: Luma Ai's careers page (Ashby), re-checked daily ## About 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. 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. Description from Luma Ai's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you. ## How to apply Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/research-scientist-engineer-reinforcement-learning-infrastructure-luma-ai-832af533) and use Apply now there to reach the employer's application. The listing is re-checked against Luma Ai's careers page daily and removed when it closes. ## Luma Ai Analyst roles on Real Analyst Jobs. [More roles at Luma Ai](https://realanalystjobs.com/companies/luma-ai) ## More like this - [Data analyst jobs in the UK](https://realanalystjobs.com/jobs/data-analyst-jobs-in-uk) - [Analyst job market report](https://realanalystjobs.com/analyst-job-market) - [What analyst roles pay](https://realanalystjobs.com/blog/data-analyst-salaries) ## Sources and links - [This role on Real Analyst Jobs](https://realanalystjobs.com/job/research-scientist-engineer-reinforcement-learning-infrastructure-luma-ai-832af533) - [Luma Ai on Real Analyst Jobs](https://realanalystjobs.com/companies/luma-ai) - [All analyst jobs](https://realanalystjobs.com/jobs)