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., * Build 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.

Reinforcement Learning, Data Management, Artificial Intelligence (AI), Systems Engineering, High Throughput, Research Skills, Application Programming Interface (API), Operating Systems, Metrics, Benchmarking, * Production AI Experience: Track record of deploying evaluations, RL loops, or sandboxed agent environments into production.

• Systems Polyglot: Deep systems background (Python, Rust, C++, Go, TypeScript, CUDA)-you pick up new tools and frameworks within days.
• San Francisco On-Site: In-person collaboration at our SF office to iterate quickly with founders and domain experts.
• Pragmatic Problem Solver: Comfortable navigating raw paper implementations, undocumented SDKs, and custom distributed training setups.