Primary record

Senior/Staff Software Engineer, Behavior Verification

Nuro Indexed employerMountain View, California (HQ) · Nuro HQ - Mountain View, CA
Source-hosted applyChecked 3h agoFull-Time
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Workplace

On-site

Employment

Full-Time

Published

Aug 11, 2026

Closes

No date supplied

The role

Who We Are

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.

About the Role

As a Senior/Staff Software Engineer working on driving behavior verification, you are responsible for implementing metrics that evaluate the end-to-end behavior of the Nuro Driver. These metrics will be used to quantify the safety of the driving behavior in our target ODD. This requires prior experience with the development or verification of behavior planning/prediction systems for robots, and a collaborative nature to work closely with a variety of teams across Nuro: Systems, Onboard Software, Simulation, Product, and Operations.

About the Work

• Develop and implement in Python generalizable metrics to verify the driving behavior of an autonomous vehicle.

• Leverage a combination of machine learning (ML) models and safety metrics from literature to evaluate the end-to-end driving behavior. <li

Requirements

Department: Systems