Staff Systems Engineer, Simulation Systems Verification and Validation Lead

• Own the end-to-end simulation V&V strategy across different levels of integration ranging from sensors-only through the full system behavior for the purpose of constructing a safety case for fully autonomous driving on public roads.

• Define the data sets, metrics, and comparison methods.

• Lead a recurring cross-functional sim validation program with simulation, perception, planning, systems engineering, safety, and data infrastructure teams. Set milestones and cadence, track risks and dependencies, and drive decisions to closure.

• Partner with statisticians and data scientists on estimators that combine simulated and real-world results into safety performance measures.

• Write the methodology documents and validation reports, and present results, risks, and recommendations to engineering and executive leadership.

• Mentor engineers across teams in V&V practice and systems thinking.

• M.S. or higher in Systems, Aerospace, Mechanical, Electrical, or Robotics Engineering, Computer Science, Physics, Mathematics, or a related field.

• 8+ years of experience in verification and validation of safety-critical systems that contain software or autonomy (for example autonomous vehicles, ADAS, aerospace guidance and flight software, or robotics).

• A record of leading technical programs across several teams, from an ambiguous problem statement to a delivered result.

• Working-level experience validating a simulation or model against real-world data so that its results could support a decision, preferably for a closed-loop system with software or a learned controller in the loop.

• Strong systems thinking: you can break a system into integration levels, define the interfaces and the evidence needed at each level, and connect component-level results to system-level claims.

• Experience deriving or implementing estimators that fuse simulated and real data, for example control variates, prediction-powered inference, Bayesian hierarchical models or power priors, importance sampling, or rare-event estimation. Similar experience with modeling uncertainties and failure rates is also acceptable.

• Experience with sensor simulation (camera, lidar, radar), neural rendering or generative scene synthesis, or perception evaluation metrics.

• Experience evaluating motion planning or behavior in closed-loop simulation at scale.

• Familiarity with model credibility and safety standards, such as NASA-STD-7009, ASME V&V 10/20/40, ISO 21448 (SOTIF), UL 4600, ISO 26262, or ISO 34502.

• Hands-on Python and SQL.

• Ph.D. in a relevant field.