Machine Learning Engineer
As a ML Engineer within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalization, comfort, and collaboration.
You’ll design and deliver ML-driven behaviours that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.
Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.
Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.
Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.
Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.
Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.
Collaborate cross-functionally across various teams to ensure integration and iteration velocity.
Mentor senior engineers and shape the long-term technical direction across Autonomy.
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.
Prior work in autonomous driving, imitation learning, or trajectory prediction.
Familiarity with personalization, human behavior modelling, or driver intent inference.
Experience integrating ML systems into production hardware or multi-agent simulation.
This is a full-time role based in our office in Japan. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.