Machine Learning Engineer

Wayve Wayve · Robotics · Leonberg, Germany · Product & Delivery

Machine Learning Engineer at Wayve, focused on developing and deploying end-to-end driving models for autonomous vehicles. Responsibilities include improving model performance, building evaluation pipelines, curating data, and influencing production deployment strategies. The role involves deep learning expertise, software engineering practices, and experience with real-time systems or robotics.

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
  • technical leadership
  • mentoring

Nice to have

  • autonomous driving
  • imitation learning
  • trajectory prediction
  • personalization
  • human behavior modeling
  • driver intent inference
  • production hardware integration
  • 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.
  • Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.

Other signals

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