Wayve

Wayve

Staff Machine Learning Engineer, AV Core

Sunnyvale, California USA · Staff+ · Full-time

Sponsorship not specified$336k-$370kDetected 60 days ago
PythonC++Machine LearningPyTorchNLPRoboticsResearchLeadershipMentoring

About the role

  • Align priorities and learn from the organisation - with AV Core, Evaluation, and Product Engineering on roadmaps and failure modes; from fleet, simulation, and product feedback; and through mentoring others on the team.

Responsibilities

  • Drive Core Model Safety roadmap themes owning the full lifecycle from research to offline/online experiments to technology transfer.
  • Train and deploy end-to-end AV 2.0 models on our global fleet, using large-scale, diverse data to validate capabilities and improve generalisation across vehicles, markets, and driving conditions.
  • Maintain awareness of the wider business context - division and company priorities, near-term product programmes, and how Core Model Safety work enables them.
  • 5+ years in ML engineering, including pathfinding in ambiguous problems - from scoping and evals to establishing a direction (and knowledge transfer) for others to build on.

Requirements

  • Experience with redundant or fallback architectures, safety-critical systems.

Compensation

  • The reasonably estimated salary for this role ranges from $336,400 to $370,300, plus a competitive equity package.

Benefits

  • Hands-on experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent.
  • Experience in 3D scene understanding and representation learning for geometric and semantic perception, large-scale semantic enrichments.
  • Build high-value open-loop and closed-loop evaluations for core capabilities and representation learning.
  • In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we're looking for the following skills and experience.

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