Machine Learning Engineer, App Sw

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

Machine Learning Engineer at Wayve, focused on developing and deploying end-to-end driving models for autonomous vehicles. The role involves improving model performance, leading projects on personalized driving, building evaluation pipelines, curating data, and influencing training and deployment strategies. Requires extensive experience shipping deep learning systems to production and expertise in sequential models, control, planning, or perception.

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

  • 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.
  • Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.

Nice to have

  • Prior work in autonomous driving, imitation learning, or trajectory prediction.
  • Familiarity with personalization, human behavior modeling, or driver intent inference.
  • Experience integrating ML systems into production hardware or multi-agent simulation.

What the JD emphasized

  • shipping deep learning systems to production
  • real-time systems or robotics
  • 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