Research Scientist / Engineer – Reinforcement Learning Infrastructure

Luma AI

. Design, build, and scale distributed RL post-training systems across thousands of GPUs .

Posted 10/8/2026full-timeRemoteMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and scaling distributed Reinforcement Learning (RL) systems, with a strong focus on post-training LLMs and high-throughput rollout generation. Proficient in building scalable RL environments and reward infrastructures while ensuring training efficiency and stability across GPU clusters.

Highest-signal resume keywords

Post-Training LLMs ExperienceDistributed PyTorch TrainingRL Environment DevelopmentGPU Clusters UnderstandingContainerization and Orchestration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement Learning (PPO/GRPO-family, RLHF, RLVR)Distributed PyTorchRL Post-Training Frameworks (veRL, OpenRLHF, TRL, Ray)Rollout Inference Engines (vLLM, SGLang)Reward Function DevelopmentEvaluation HarnessesSandboxed ExecutionMulti-Turn Tool UseNCCLMPI

Tools & Technologies

KubernetesRay

Industry Keywords

Large-Scale RLGPU ClustersNetworkingCommunication LibrariesResearch Contributions in RL

Tech Stack

Tools & technologies

KubernetesPyTorchRay

About the role

Key responsibilities & impact

Design, build, and scale distributed RL post-training systems across thousands of GPUs
Build high-throughput rollout generation integrating vLLM, SGLang, weight synchronization, and asynchronous/off-policy schemes
Design scalable RL environments for agentic, multi-step tasks including sandboxed code execution, tool use, computer use, and multimodal interaction
Build reward infrastructure including verifiable/programmatic rewards, reward-model serving, LLM-as-judge pipelines, and defenses against reward hacking
Develop evaluation, monitoring, and debugging tooling for stable large-scale RL runs
Advance training efficiency and stability and turn post-training ideas into production runs with researchers
Learn the current RL stack, diagnose bottlenecks, ship and validate improvements, and harden the full loop across thousands of GPUs

Requirements

What you’ll need

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
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

Benefits

Comp & perks

Equal opportunity employer
Flexible remote work arrangement in the EU