Software Engineer, RL Training Infra

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.