Senior System Engineer- AI Safety

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 AI System Safety Engineer will own the safety assurance lifecycle for the AI system powering our humanoid robot platform operating in environments alongside humans. 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 model-driven behaviors, define AI safety requirements, lead verification of AI model safety properties, 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.

Core Responsibilities

• Lead AI-specific hazard identification and risk assessment sessions using STPA (Systems-Theoretic Process Analysis), FMEA, and scenario-based analysis to surface AI-related failure modes of perception

• Assess risks introduced by AI model updates and retraining cycles, including regression of safety-critical behaviors

• Identify unsafe control actions that can arise from AI decision-making across all robot operating modes: autonomous navigation, collaborative manipulation, human handoff, and degraded/safe-state operation

• Establish safety requirements for AI model inputs, outputs, confidence thresholds, uncertainty quantification, and out-of-distribution detection

• Develop test cases for verification and validation of AI safety

• Define, engineer, deploy and employ system safety validation cells instrumented to collect artifacts to be reviewed by safety assessor

• Maintain full traceability from safety goals through AI system requirements to test cases and evidence artifacts

Required Qualifications

• B.S. Computer Science, Systems Engineering or Robotics

• Experience designing AI-based safety systems for complex electromechanical products with significant safety aspects

• Demonstrated understanding of AI/ML failure modes relevant to physical systems — distributional shift, model uncertainty, unsafe generalization — and practical methods for measuring and mitigating them