Software Engineer, RL Training Infra
Build and maintain infrastructure for large-scale RL training runs of frontier OpenAI models. Debug across training, inference, and distributed systems while supporting research integrations.
About the job
Responsibilities
- Keep large-scale RL training runs moving by jumping into the most urgent engineering and infrastructure problems.
- Debug issues across training systems, inference, orchestration, scaling, and distributed infrastructure.
- Solve hard technical problems at the boundary between research and engineering: scaling experiments, improving training reliability, debugging distributed systems, reducing latency and cost, and making new capabilities robust under real workloads.
- Improve reliability and efficiency for RL training runs.
- Help researchers who are developing infra-heavy integrations, such as multi-agent capabilities or memory.
- Turn recurring operational issues into better tools, systems, processes, or abstractions.
- Work closely with research, infrastructure, and partner teams during tight model run timelines.
- Become useful quickly in messy, ambiguous areas where ownership matters more than a perfectly scoped project.
- Debug failures that cut across model behavior, training data, RL systems, evaluation infrastructure, serving systems, and agent harnesses, then turn those failures into hypotheses, fixes, and durable improvements.
Requirements
- Strong generalist engineer with experience in some layer of ML infrastructure.
- Experience working on RL, inference, scaling, training systems, orchestration, or adjacent ML infrastructure.
- Ability to learn extremely quickly and operate across unfamiliar layers.
- Strong debugger with high ownership, low ego, and excellent communication.
- Comfortable landing in a messy area with tight timelines, becoming useful quickly, and gradually raising the quality of the whole system.
- Energized by fast-moving environments where reliability, speed, and judgment matter.
- Like building load-bearing systems and processes when that is what the team needs.
Nice to Haves
- Experience supporting large-scale model training, async RL systems, or high-throughput ML infrastructure.
- Experience debugging distributed systems across GPUs, networking, orchestration, or inference stacks.
- Background in performance optimization, scaling, or production-critical infrastructure.
- Experience working directly with researchers or fast-moving model teams.
Skills
Reinforcement Learning, ML Infrastructure, Distributed Systems, Gpu Debugging, Orchestration, Inference Systems, Scaling, Training Systems, Performance Optimization, Async Rl
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