Lead Simulation & Autonomy Systems Architect

• 10+ years of experience in autonomous systems, such as robotaxi, autonomous trucking, mining, agriculture or construction autonomy.
• Hands-on ownership of behavior, planning or control software in a deployed autonomy stack, including proven design of minimum-risk maneuvers, fallback trajectories and degraded modes.
• Experience integrating simulation into the development and validation loop, including scenarios, sensor models and data contracts between simulation and the autonomy stack (NVIDIA Isaac Sim, CARLA or equivalent).
• Experience with FMEA, hazard analysis and close collaboration with functional safety teams (ISO 26262, ISO 21448 SOTIF or similar).
• Understanding of vehicle and powertrain dynamics, including braking and traction limits under load, on slopes and in rough terrain.
• Software architecture and hardware/software integration experience for real-time systems on embedded compute.
• Continues to write code in C++ and Python and is comfortable working through AI coding agents.

Nice to have:

• Experience with autonomous trucks, mining haul or construction equipment, and field and rapid-response engineering that turns field issues into fast fixes.
• Experience with end-to-end learned driving or VLA models and wrapping them in safety envelopes.
• Experience with NVIDIA Omniverse and synthetic data generation for training and testing.
• Experience doing more with less while managing several teams, and patents or publications in autonomy, safety or control.

Simulation Strategy:

• Own how simulation supports autonomy development and validation, including scenario coverage, combining real and simulated data, and validating degraded modes and safety scenarios.

Failure Analysis & Safety Case:

• Lead FMEA and fault-tree analysis across perception, planning, control and hardware.
• Map each failure to a detection mechanism and safe response.
• Work with safety engineering on a safety case covering acceptable fleet event rates and evidence of due diligence.

Degraded Modes & Reactive Safety:

• Design health monitors, run levels, fail-operational and fail-safe behavior, minimum-risk conditions, and fallback paths for a simpler system.
• Design fast reactive layers, such as collision-imminent braking that does not wait for the full perception pipeline, while keeping false positives low.

Cross-Cutting System Architecture:

• Define software/hardware integration, interfaces and system-level features spanning teams.
• Bridge hardware and software timelines by advising hardware teams which requirements must be fixed now and which can wait.

Hands-On Code with AI Agents:

• Write, review and guide code produced with AI coding agents, using guardrails such as automated tests and toolchain checks.
• Take degraded-mode architecture through implementation and testing on real machines.

Roadmap & Team Leadership:

• Lead the simulation workstream’s contribution to the 2027 program charter and roadmap.
• Guide two Simulation & Perception architects and mentor engineers.

• Availability for pre-approved trips to client sites.