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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The Core Model Safety team develops foundational capabilities for assisted and automated driving, including collision avoidance, model understanding, and robustness under failure modes.
As a Staff Machine Learning Engineer in AV Core, you will lead the technical direction and delivery of learned emergency manoeuvre prediction and collision detection models. Emergency manoeuvres are rare, high-consequence events that demand sophisticated approaches to data, modelling, and validation. You'll own the full program lifecycle from problem definition through modelling, evaluation, integration, and evidence for deployment.
Key responsibilities include:
- Driving Core Model Safety roadmap themes with full ownership from research through offline/online experiments to technology transfer
- Training and deploying end-to-end AV 2.0 models for emergency manoeuvre prediction and collision detection on the global fleet, using large-scale diverse data to validate capabilities and improve generalization across vehicles, markets, and driving conditions
- Collaborating on online occupancy models for geometric and semantic perception
- Building high-value open-loop and closed-loop evaluations for core capabilities and representation learning
- Aligning priorities across AV Core, Evaluation, and Product Engineering teams; learning from fleet, simulation, and product feedback; and mentoring team members
- Maintaining awareness of business context, division and company priorities, near-term product programs, and how Core Model Safety enables them
You'll work in a focused, high-impact senior team with strong ownership, access to large-scale training and fleet data, and close partnerships with research, simulation, evaluation, and applied engineering teams.
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company offers relocation support, visa sponsorship, learning and development budgets, comprehensive benefits including health insurance, dental, enhanced parental leave, and retirement/pension plans.
REQUIREMENTS:
Essential:
- Strong track record of ML engineering, including pathfinding in ambiguous problems—from scoping and evals to establishing direction and knowledge transfer for others
- Hands-on experience with ML systems deployed in the real world
- Proficiency in Python and PyTorch with strong software engineering practices and hands-on experience building reliable machine learning training and evaluation systems
- Excellent experimental judgement: able to turn ambiguous behavioral problems into falsifiable hypotheses, useful metrics, disciplined ablations, and clear technical decisions
- Senior-level ownership and collaboration: able to lead substantial technical areas, work across research and engineering boundaries, and communicate clearly in writing and verbally
Desirable:
- Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems
- Experience in 3D scene understanding and representation learning for geometric and semantic perception, large-scale semantic enrichments
- Experience mining, generating, or evaluating rare events using simulation and fleet or heterogeneous real-world data
- Experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent
- Proficiency in C++, CUDA, distributed training, or performance optimization for production machine learning systems
