Nuro

Senior Software Engineer, Collision Avoidance Testing

Mountain View, California (HQ) · Senior

Sponsorship not specifiedDetected 35 days ago

Systems EngineeringRoboticsMechanical DesignElectrical EngineeringLeadership

About the role

You will analyse the results, recommend product changes, and report on residual risk both to leadership and in the safety case.
Author test and residual risk reports, and make strategic recommendations based on the results for improving simulation or test realism and autonomy architecture.
Highly collaborative in nature with strong abilities to think and communicate analytically and effectively, both verbal and written.

Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world.
Safer streets, more time for what matters, and easier access to the world around us, that's why we're building a universal autonomy platform: self-driving for all roads and all rides.
You will be responsible for identifying gaps in onroad and simulation tests, designing appropriate tests to fill those gaps, reviewing test outcomes, and iterating on test design.
Develop validation strategies to test Automated Emergency Braking functions using simulation and structured testing.
Identify gaps in simulation and public roads testing and design structured test scenarios to address these gaps and by leveraging appropriate standards from NHTSA or NCAP.
Support the building of and using models to quantify risk in scenarios with non-zero probability of collision.
Support the safety case by identifying, documenting, and communicating collision avoidance and simulation or test realism claims.

Bachelor's Degree, Master's Degree, or Doctoral Degree in Computer Science, Math, Electrical Engineering, Mechanical Engineering, Robotics, Physics, or related field.
Experience with designing simulation-based evaluation suites and scenarios for testing autonomous vehicles using well-defined metrics to score good behavior.
5+ years of technical work experience in a relevant area, of which 3+ years should be in robotics SW.
Working knowledge of and ability to apply systems engineering concepts of requirements, verification and validation.

Experience with developing collision metrics for self-driving vehicles.
Experience with structured or physical testing of robots or vehicles.
Experience writing safety claims and system requirements for SAE L3/L4 systems to operate safely.
Hands-on experience with STPA and ISO 21448.

Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that's why we're building a universal autonomy platform: self-driving for all roads and all rides.
Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles.
With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected.
Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors.
Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets.
Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles.
Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T.
Rowe Price, and other leading investors.

This listing is sourced directly from Nuro's careers page and normalized into a canonical job model.