Grafton Sciences is building physical general intelligence — autonomous systems that can experiment, reason, and discover in the physical world. The Senior RL Research Scientist will design and train reinforcement learning systems that optimize tool control, process tuning, and long-horizon workflows.
Requirements
- 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.
Responsibilities
- 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.
Other
- We look for candidates who can demonstrate world-class excellence.
- Competitive salary, meaningful equity, and benefits.
