Vehicle Validation Engineer

Nuro•Mountain View, CA

1d•$132,300 - $198,450

About The Position

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future. The team comprises a group of engineers committed to the validation and enhancement of autonomy at the vehicle level. Responsibilities include the formulation of technical requirements, design revisions, and the implementation of countermeasures through systematic plan development, test execution, and comprehensive data analysis. The team operates cross-functionally to validate autonomy performance and conducts rigorous root cause analysis to resolve complex technical issues. Define validation objectives: Transform high-level company goals and ODD into verifiable requirements, identifying coverage gaps to ensure testing reflects actual design intent. Develop analytical frameworks: Engineer first-principles models to evaluate system behavior against performance targets, assessing whether specifications meet underlying intent. Establish validation criteria: Define measurable pass/fail standards grounded in rigorous engineering reasoning rather than arbitrary thresholds. Architect test plans: Scope data needs, resources, and instrumentation requirements to systematically execute validation methodologies. Execute field operations: Lead complex test campaigns, ensuring data integrity and adapting protocols dynamically during collection. Drive cross-functional strategy: Communicate validation risk and trade-offs to leadership, providing technical recommendations to navigate competing priorities. Engineer data tooling: Develop scripts, pipelines, and automation to process validation data, leveraging hands-on coding or AI-assisted development.

Requirements

Professional background in vehicle validation, autonomous systems verification, or a comparable engineering field.
Proven capability to architect analytical validation frameworks grounded in first principles, moving beyond the execution of static test procedures.
Aptitude for end-to-end validation ownership, encompassing requirement identification, rigorous test execution, and technical reporting.
Exceptional cross-functional communication skills, with the ability to synthesize and reconcile divergent perspectives from engineering, product, and safety stakeholders.
Proactive approach to closing operational gaps, demonstrating the technical maturity to identify and resolve systemic needs outside of immediate scope.

Nice To Haves

Foundational expertise in vehicle dynamics to support high-fidelity performance modeling and the development of analytical validation methodologies.
Functional understanding of ADAS or autonomous stacks, particularly the validation nuances inherent to perception, motion planning, or control systems.
Demonstrated experience architecting validation roadmaps and managing complex program timelines to ensure systematic milestone delivery.
Technical familiarity with HIL/SIL simulation environments, automated test frameworks, and large-scale data collection infrastructure.

Responsibilities

Define validation objectives: Transform high-level company goals and ODD into verifiable requirements, identifying coverage gaps to ensure testing reflects actual design intent.
Develop analytical frameworks: Engineer first-principles models to evaluate system behavior against performance targets, assessing whether specifications meet underlying intent.
Establish validation criteria: Define measurable pass/fail standards grounded in rigorous engineering reasoning rather than arbitrary thresholds.
Architect test plans: Scope data needs, resources, and instrumentation requirements to systematically execute validation methodologies.
Execute field operations: Lead complex test campaigns, ensuring data integrity and adapting protocols dynamically during collection.
Drive cross-functional strategy: Communicate validation risk and trade-offs to leadership, providing technical recommendations to navigate competing priorities.
Engineer data tooling: Develop scripts, pipelines, and automation to process validation data, leveraging hands-on coding or AI-assisted development.