Principal Machine Learning Engineer, App Sw

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

Principal ML Engineer at Wayve, focusing on developing and deploying end-to-end driving models for autonomous vehicles. The role involves leading initiatives in personalized and collaborative driving, building evaluation pipelines, curating data, and influencing ML system design for production.

What you'd actually do

  1. Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.
  2. Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.
  3. Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.
  4. Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.
  5. Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.

Skills

Required

  • Deep learning
  • Sequential models
  • Control
  • Planning
  • Perception
  • Python
  • C++
  • CUDA
  • PyTorch
  • Software engineering practices
  • Real-time systems
  • Robotics
  • Simulation
  • Vehicle-in-the-loop components
  • Leadership
  • Mentoring

Nice to have

  • Autonomous driving
  • Imitation learning
  • Trajectory prediction
  • Personalization
  • Human behavior modeling
  • Driver intent inference
  • ML systems integration
  • Production hardware
  • Multi-agent simulation

What the JD emphasized

  • Extensive and proven track record of shipping deep learning systems to production.
  • Expert in deep learning (esp. sequential models, control, planning, or perception).
  • Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.
  • Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.

Other signals

  • develop and improve end-to-end driving models
  • lead projects on personalized and collaborative driving
  • build evaluation pipelines and metrics
  • curate and mine real-world and synthetic data
  • influence architecture choices, training methodologies, and deployment pathways