• Build RL environments for optimization, process tuning, and tool orchestration using real-world simulation and digital twins.
• Design and implement safe RL methods, verifier-integrated rewards, offline→online transitions, and policy evaluation pipelines.
• Develop state representations, action abstractions, and constraint mechanisms for reliable long-horizon decision-making.
• Collaborate with LLM researchers, agent systems, simulation teams, and tooling engineers to deploy RL agents into real workflows.

Qualifications

• Strong background in reinforcement learning, optimal control, or sequential decision-making, with experience applying RL to complex real or simulated systems.
• Familiarity with safe RL, constrained RL, verifier/detector integration, or multi-step policy evaluation frameworks.
• Demonstrated ability to build RL environments, design reward structures, and diagnose policy behavior at scale.
• Comfortable working across ML, simulation, systems engineering, and physical-toolchain interfaces in a fast-paced research environment.

Above all, we look for candidates who can demonstrate world-class excellence. Compensation We offer competitive salary, meaningful equity, and benefits.