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
Contribute to safety-critical features, including fall prevention and recovery strategies
Required Qualifications
MS or PhD in Robotics, Control Systems, Mechanical Engineering, or related field
Strong background in dynamics, kinematics, and control theory
Experience with humanoid or legged robot control systems
Proficiency in Python for data analysis
Hands-on experience with simulation tools (e.g., MuJoCo, Gazebo, Isaac Sim)
Familiarity with state estimation techniques (e.g., Kalman filters, sensor fusion)
Hands-on experience with data collection and data analysis in Verification and Validation
Preferred Qualifications
Experience with whole-body control frameworks and optimization-based control (QP, MPC)
Knowledge of contact dynamics and multi-body systems
Hands-on experience with dFMEA, STPA, Fault Tree Analysis
Experience with reinforcement learning applied to locomotion or balance
Proficiency in C with language extensions for MISRA C for real-time systems
Experience with applying ISO/TS 15066 and ISO/TS 25785-1
Background in safety-critical or functional safety systems (e.g., ISO 26262, IEC 61508)
Experience deploying algorithms on embedded or edge compute platforms