Machine Learning Engineer, Driving Product - Wayve

Develop the AI driver model architecture and training algorithms to introduce and enhance the driving 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. Proven experience training deep learning models, with clear end-to-end ownership (data, training, evaluation, iteration). Experience taking ML models into production, including working through real-world constraints and quality and safety requirements 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 physical systems. Experience with a Pytorch lightning training infrastructure Experience with model compression and deployments to embedded systems

Develop the AI driver model architecture and training algorithms to introduce and enhance the driving 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.

Develop the AI driver model architecture and training algorithms to introduce and enhance the driving 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. Proven experience training deep learning models, with clear end-to-end ownership (data, training, evaluation, iteration). Experience taking ML models into production, including working through real-world constraints and quality and safety requirements 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 physical systems. Experience with a Pytorch lightning training infrastructure Experience with model compression and deployments to embedded systems