Senior RL Systems Engineer - Scaled Post-Training Infra

Speedrun Talent Network; Eu; Hybrid; EUR 120,000 - 160,000; Full time

Job summary

Luma is seeking a senior RL systems engineer to design and scale post-training RL pipelines across thousands of GPUs. You will orchestrate trainer, rollout, and environment workloads, integrating inference engines and reward systems to drive robust, efficient learning at frontier scale.

You will build environments for agentic, multi-step tasks, and develop the tooling to monitor, evaluate, and debug large RL runs. Collaboration with researchers is key to turning ideas into production.

Qualifications

• Experience with 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.

Responsibilities

• 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.

Skills

Post-training RL with LLMs

Distributed PyTorch training

RL environments & reward functions

RL post-training frameworks

GPU clusters & NCCL/MPI

Rollout inference engines

Tools

veRL

OpenRLHF

TRL

Ray orchestration

vLLM

SGLang

Job description

Luma is seeking a senior RL systems engineer to design and scale post-training RL pipelines across thousands of GPUs. You will orchestrate trainer, rollout, and environment workloads, integrating inference engines and reward systems to drive robust, efficient learning at frontier scale.

You will build environments for agentic, multi-step tasks, and develop the tooling to monitor, evaluate, and debug large RL runs. Collaboration with researchers is key to turning ideas into production.