System Safety Engineer

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.

Summary
This is a Senior MTS position reporting directly to the Head of Safety and Certification. The Safety Systems Engineer-Robot Stability will own the design, implement, and optimize control systems that enable robust balance, posture maintenance, and dynamic locomotion in humanoid robots.

This role sits at the intersection of AI, functional safety, and robotics — responsible for ensuring that the robot's AI-driven perception, planning, and control behaviors meet the requirements of the broader humanoid robot safety standard ecosystem. You will assess risks introduced by dynamic robot behaviors, define robot safety requirements for stability, lead verification through data collection and analysis, and build the evidence packages needed for formal certification. The role requires a technical executor with a bias for action, impeccable rigor, and the ability to drive cross-disciplinary teams. The candidate must demonstrate deep technical credibility.

Key Responsibilities

• Develop safety requirements for Robot Stability through analysis, empirical derivation, and testing

• Derive requirements for performance and stability of advanced control algorithms for whole-body stability, balance, and posture regulation in humanoid platforms

• Architect fail-safe methodology for robot stability to influence design and implementation of real-time controllers (e.g., ZMP, MPC, inverse dynamics, whole-body control)

• Define requirements, performance and fail-safe methods for sensor feedback (IMU, force/torque, vision) for state estimation and disturbance rejection

• Invent novel and improve existing techniques for robustness against external perturbations, uneven terrain, and dynamic interactions

• Collaborate with perception, planning, and hardware teams to ensure seamless system integration

• Simulate, test, and validate control strategies in both virtual environments and physical robots