Senior RL Infrastructure Engineer: Scale-out GPU Systems

lumaai; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

lumaai is seeking a frontier research engineering role focused on systems to scale reinforcement learning at truly large scale. You will work on the full RL loop—training, rollout generation, environment execution, and reward computation—across thousands of GPUs to ensure throughput, stability, and correctness in production settings.

The role spans post-training LLMs with PPO/GRPO-family methods, RLHF, or RLVR, and involves building environments, reward models, verifiers, and evaluation

Qualifications

• Hands-on experience post-training large language models with RL (PPO/GRPO-family, RLHF, RLVR).
• Extensive distributed PyTorch training with FSDP, Tensor Parallel, Pipeline/Expert Parallel.
• Background building RL environments, reward functions, verifiers, and evaluation harnesses for LLM agents.

Responsibilities

• Design and scale distributed RL post-training systems across thousands of GPUs.
• Build high-throughput rollout generation with inference engines and asynchronous schemes.
• Develop RL environments with sandboxed execution and multi-turn tool use.
• Construct reward infrastructure, including LLM-as-judge pipelines and defenses against reward hacking.
• Build evaluation, monitoring, and debugging tooling for long RL runs.
• Improve training efficiency and stability and deploy new ideas into production with researchers.

Skills

Post-training RL with LLMs

Distributed PyTorch training

RL environments and reward functions

RLHF/RLVR/GRPO methods

GPU cluster management

Tools

veRL

OpenRLHF

TRL

Ray orchestration

Job description

lumaai is seeking a frontier research engineering role focused on systems to scale reinforcement learning at truly large scale. You will work on the full RL loop—training, rollout generation, environment execution, and reward computation—across thousands of GPUs to ensure throughput, stability, and correctness in production settings.

The role spans post-training LLMs with PPO/GRPO-family methods, RLHF, or RLVR, and involves building environments, reward models, verifiers, and evaluation