Principal Validation Analyst - Autonomous Vehicles

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Principal Validation Analyst Engineer for Autonomous Vehicles at NVIDIA. This role focuses on developing and owning the system architecture for AV evaluation and validation, including data ingestion, metric calculation, and reporting. The engineer will establish technical standards, lead cross-team efforts, and architect scalable cloud and GPU-accelerated compute strategies for large-scale metric evaluation. The role also involves mentoring engineers and representing NVIDIA's validation approach externally.

What you'd actually do

  1. Develop and take full responsibility for the complete system architecture for AV evaluation and validation, proactively driving progress across multiple teams within the BU. This includes data ingestion, metric calculation, behavioral assessment, and reporting for thousands of test miles daily.
  2. Establish technical standards, build patterns, and guidelines for evaluation frameworks and data pipelines across the V&V organization and the broader Automotive BU. Champion root-cause analysis practices that identify systemic process issues and drive corrective action.
  3. Lead large, cross-team efforts that span multiple organizations — aligning evaluation infrastructure with requirements from Software Product, Development, Safety, and Testing teams.
  4. Architect scalable cloud and GPU-accelerated compute strategies for large-scale metric evaluation, bringing to bear NVIDIA's outstanding strengths in accelerated and distributed computing.
  5. Mentor and develop engineers across the V&V team and adjacent organizations, setting a high bar for code quality, system architecture, and validation methodology. Arbitrate cross-team technical decisions and guide resolution of complex architectural trade-offs.

Skills

Required

  • MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent experience
  • 15+ years of professional experience in software engineering, data engineering, or systems architecture
  • Demonstrated ability to own and define system-level architecture for complex software platforms
  • Deep expertise in Python and C/C++
  • Established experience in defining technical direction across organizational boundaries
  • Hands-on experience building and scaling data pipelines, evaluation infrastructure, or analytics platforms in cloud computing environments
  • Strong leadership and communication skills
  • Ability to produce clear, high-quality documentation

Nice to have

  • Deep background in autonomous vehicle validation, ADAS evaluation, or safety-critical systems verification
  • Experience defining validation methodology, safety metrics, or ODD coverage frameworks
  • Demonstrated ability in developing and growing GPU-accelerated or distributed computing pipelines
  • Publications, patents, or industry specifications contributions in areas related to AV safety, validation, or large-scale data analysis
  • Experience guiding technology strategy at the BU level or above
  • Strong background in statistics and quantitative analysis

What the JD emphasized

  • 15+ years of professional experience in software engineering, data engineering, or systems architecture, with sustained performance at a senior or staff level.
  • Demonstrated ability to own and define system-level architecture for complex software platforms — not just implement features, but compose entire modules or systems that teams build on.
  • Deep expertise in Python and C/C++
  • Established experience in defining technical direction across organizational boundaries
  • Hands-on experience building and scaling data pipelines, evaluation infrastructure, or analytics platforms
  • Deep background in autonomous vehicle validation, ADAS evaluation, or safety-critical systems verification
  • Experience defining validation methodology, safety metrics, or ODD coverage frameworks that have been adopted across an organization or industry body.

Other signals

  • Develop and take full responsibility for the complete system architecture for AV evaluation and validation
  • Establish technical standards, build patterns, and guidelines for evaluation frameworks and data pipelines
  • Architect scalable cloud and GPU-accelerated compute strategies for large-scale metric evaluation
  • Hands-on experience building and scaling data pipelines, evaluation infrastructure, or analytics platforms