Technical Program Manager, Evaluation & Validation

Wayve Wayve · Robotics · Sunnyvale, CA · Product & Strategy

Technical Program Manager to drive delivery across simulation, evaluation, and validation platforms for Embodied AI technology in autonomous vehicles. This role gates real on-road releases and requires strong technical understanding of ML and validation processes.

What you'd actually do

  1. Drive program delivery: Own end-to-end delivery of cross-team programs spanning simulation, evaluation, and validation.
  2. Sequence cross-cutting initiatives: Compose roadmaps across simulators, datasets, evaluation, and triage; manage dependencies, risks, and trade-offs, and escalate early with clear options.
  3. Tie work to outcomes: Connect platform delivery to measurable outcomes - developer velocity, signal quality, cost per evaluation, time-to-insight, and validation credibility.
  4. Coordinate release-gating validation: Partner with Autonomy, Science, Release, Product, and Safety on acceptance criteria and the validation that gates releases.
  5. Run delivery rhythms: Run planning, reviews, and retros, and communicate progress and risk to engineers, partners, and senior leaders.

Skills

Required

  • 8+ years leading complex, multi-team programs
  • Credible with engineers and scientists across simulation, evaluation, ML, and validation
  • Effective in ambiguous, safety-critical environments
  • Skilled at planning, prioritising, and managing risk and dependencies across teams
  • Influence without authority across Autonomy, Science, Release, Product, and Safety
  • Comfortable defining and using metrics - signal quality, coverage, time-to-insight - to steer delivery and decisions
  • Systems thinking

Nice to have

  • Experience in autonomous vehicles, robotics, or another safety-critical AI/ML program
  • Familiarity with simulation, synthetic data, evaluation harnesses, or ML observability tooling at production scale
  • Working knowledge of SOTIF (ISO 21448), ISO 26262, or other safety standards relevant to validating learned systems
  • An engineering or computer science degree, or experience working as an engineer

What the JD emphasized

  • prove the AI Driver is ready for the road
  • gate real on-road releases
  • safety-critical
  • validation that gates releases

Other signals

  • driving delivery across platforms and processes that prove the AI Driver is ready for the road
  • programs gate real on-road releases
  • partner with Autonomy, Science, Release, Product, and Safety on acceptance criteria and the validation that gates releases