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.