Research Engineer, RL Environments and Infrastructure

We are looking for a hybrid Systems Engineer and AI Researcher to lead the development of our agent evaluation framework and post-training data pipelines. You will design sandboxed execution environments, high-throughput reinforcement learning feedback loops, and robust evaluation setups that handle non-deterministic agent outputs. Core ResponsibilitiesBuild Stateful Agent Environments: Design deterministically verifiable, stateful sandboxes (web, OS, API, database) where agents can execute 50+ step action trajectories safely. Scale Post-Training & RL Pipelines: Implement high-throughput post-training infrastructure (RLHF, Direct Preference Optimization, Process-Supervised Reward Models) for dynamic policy optimization. Design Enterprise Benchmarks: Formulate evaluation metrics and automated grading harnesses that catch agent drift, hallucination, and loops in realistic enterprise environments. Systems Optimization: Keep latency low and compute efficiency high across distributed GPUs and sandboxed runtime environments.