Senior Autonomy Engineer

This role demands deep technical expertise, mathematical rigor, and first-principles thinking to ensure safety in autonomous driving. You will own critical elements of our overall safety strategy to deliver safe, transformative autonomous trucking systems.

The Autonomy Safety team is charged with an incredibly challenging task: how to make — and prove — that an autonomous truck is incredibly safe (fewer than one fatal collision per 10⁸ hours). There is no textbook for this problem. You will be part of a small, high-impact team working at the frontier of safety, leveraging existing methods and pioneering new ones when needed.

• →Write C++ to efficiently search high-dimensional failure spaces at scale
• →Use first-principles analysis to develop requirements for safe perception, localization, prediction, motion planning, and control, collaborating directly with the Autonomy Software teams building those systems
• →Identify failure modes and edge cases that expose weaknesses in our system before they appear in on-road environments.
• →Perform hazard analysis, risk assessments, and fault tree analysis that feed directly into our safety case and drive system design decisions
• →Provide analysis to support complex autonomy system design trade-offs that affect safety and performance
• →Lead and support structured testing campaigns like simulation, track, on-road, and hardware-in-the-loop to validate that our safety claims hold
• →Pioneer new methods when existing ones aren't enough to hit our safety targets
• →Drive safety thinking cross-functionally — this team doesn't sit in a corner; it shapes how everyone builds

What you’ll bring:

• →M.S. or Ph.D. in engineering, mathematics, statistics, or a related technical field
• →Deep understanding of kinematics, dynamics, and system modeling
• →Strong foundation in probability and statistics
• →Strong programming skills in C++ and/or Python
• →Hands-on work across one or more of the following:
  • →Sensor fusion and object tracking (e.g., Kalman filtering, particle filters, least squares estimation)
  • →Machine learning methods (e.g., neural networks, SVMs, kNN, regression models, decision trees)
  • →Classical computer vision (e.g., edge/corner detection, camera calibration, optics)
  • →Motion planning (e.g., A*, RRT, potential fields)
  • →Control systems (e.g., PID, nonlinear control, model predictive control)
• →Excellent verbal and written communication skills, particularly on technical topics
• →Safety analysis methodologies: FTA, FMEA, HARA
• →Safety-critical standards: ISO 26262, DO-178, IEC 61508, SOTIF, UL 4600
• →Redundancy strategies and fault-tolerant system design