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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The Application Engineering team works on critical initiatives pushing the frontier of model-based autonomous driving, covering core driving performance and feature-level intelligence such as personalization, comfort, and collaboration.
As an ML Engineer on this team, you will design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work spans model architecture, data pipelines, evaluation frameworks, and real-world deployment. You'll collaborate closely with the AI Platform, Simulation, Robot Software, and Model Release teams to build systems that are performant, adaptable, and production-ready.
Key responsibilities include:
- Developing and improving end-to-end driving models with state-of-the-art performance, robustness, and generalization
- Driving projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment
- Building evaluation pipelines and metrics for closed-loop and open-loop driving performance and product readiness
- Curating and mining real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development
- Influencing architecture choices, training methodologies, and deployment pathways for production-scale learning systems
- Collaborating across teams to keep integration smooth and iteration fast
- Mentoring engineers and helping shape long-term technical direction across Autonomy
Wayve operates a hybrid working model combining in-person collaboration in dedicated office spaces with focused remote work. The company has main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The role offers relocation support and visa sponsorship where applicable, learning and development budgets, comprehensive benefits including health insurance, dental, enhanced parental leave, retirement/pension, and access to wellbeing resources.
REQUIREMENTS:
Essential:
- Extensive and proven track record of shipping deep learning systems to production
- Expert in deep learning, especially sequential models, control, planning, or perception
- Proficient in Python and other relevant languages (C++, CUDA) and ML frameworks (especially PyTorch), with solid software engineering practices foundation
- 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
Desirable:
- 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
