Senior RL and Physical Systems Engineer at Grafton Sciences

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

08 Mar, 26

Salary

0.0

Posted On

08 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Robotics, Physical System Modeling, Controls, High-Performance Automation, Reinforcement Learning, Simulation, Machine Learning, Prototyping, Testing, Verification Tools, Interdisciplinary Collaboration, Hardware Integration, Safety Checks, Evaluation Frameworks, Physical Experimentation

Industry

Robotics Engineering

Description

About Grafton Sciences We’re building AI systems with general physical ability — the capacity to experiment, engineer, or manufacture anything. We believe achieving this is a key step towards building superintelligence. With deep technical roots and real-world progress at scale (e.g., a $42M NIH project), we’re pushing the frontier of physical AI. Joining us means inventing from first principles, owning real systems end-to-end, and helping build a capability the world has never had before. About the Role We’re seeking a Senior RL and Physical / Robotics Systems Engineer to build the environments, verifiers, and physical-process models that allow agents to learn and operate in the real world. You’ll work across robotics, ML, simulation, and physical experimentation to develop high-fidelity RL environments, evaluation frameworks, and automated verification systems. This role is ideal for someone who has worked deeply with hardware and robotics while also building ML systems for control, planning, or physical process optimization. Responsibilities • Build RL environments, simulation interfaces, and evaluation workflows for complex physical processes. • Develop verifiers, safety checks, and automated evaluation tools for learning systems interacting with real or simulated hardware. • Work across robotics, controls, simulation, and ML teams to integrate hardware intuition into learning-based systems. • Prototype, test, and refine physical workflows, combining real hardware data with learned models. • Own cross-stack engineering tasks spanning robotics, software, ML frameworks, and system-level integration. Qualifications • Strong background in robotics, physical system modeling, controls, or high-performance automation. • Experience building RL environments, evaluators, or simulation-based training systems. • Ability to work across the full stack of physical engineering, from prototypes to software abstractions. • Comfort integrating ML models with real-world hardware and designing verification tools. • High-agency engineer who thrives in interdisciplinary, hands-on environments. Above all, we look for candidates who can demonstrate world-class excellence. Compensation We offer competitive salary, meaningful equity, and benefits.

Responsibilities

The role involves building reinforcement learning environments and automated verification systems for agents operating in real-world scenarios. You will also prototype and refine physical workflows, integrating hardware data with learned models.