The role
We are hiring Senior and Staff ML Engineers that sit within Safety Systems / autonomy organisations, building critical behaviours for Level 2 to level 4 driving. It is highly product-focused: you will train and iterate on end-to-end driving models and get them shipped into the car on a fast timeline measured in months. The opportunity is to own the full loop from data and training through evaluation and on-road validation, with direct impact on high-priority commercial deliveries (including the Nissan MVP). You will join a small, high-ownership teams at the point of rapid growth, where execution and real-world outcomes matter.
Key responsibilities
Develop the AI driver model architecture and training algorithms to introduce and enhance the safety behaviors for L2/L3/L4
Own key parts of the training model lifecycle, including evaluation strategy, success metrics, and iteration planning.
Mine, bucket, and curate real-world and synthetic data to teach specific driving behaviours, and implement data schemes to support training.
Run and analyse on-road and offline experiments, translate results into clear next steps, and drive improvements through repeated training cycles.
About you
In order to set you up for success as a Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.
Essential
Proven experience training deep learning models, with clear end-to-end ownership (data, training, evaluation, iteration).
Proven Experience taking ML models into production, including working through real-world constraints and quality and safety requirements.
Desirable
Reinforcement learning experience (especially where it materially improved real-world performance).
Experience with end-to-end driving models and / or transformer networks
Automotive or OEM experience, or prior work that involved deploying ML into safety-critical systems.
Experience with a Pytorch lightning training infrastructure
This is a full-time role based in our office in Israel. 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.