Staff Machine Learning Engineer, AV Core

About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career!

The role

As a Staff Machine Learning Engineer on Wayve’s Core Model Safety team in AV Core, you will help shape what our end-to-end driving model must understand to be safe and reliable in the real world - and turn that into trained capabilities, clear evidence, and adoption on the shared backbone across core and product engineering. The Core Model Safety team builds foundational capabilities for assisted and automated driving - collision avoidance, scene understanding, model understanding, and robustness under failure. You will work in a focused, high-impact senior team with strong ownership, access to large-scale training and fleet data, and close partners in research, simulation, evaluation, and applied engineering.

Key responsibilities

• Drive Core Model Safety roadmap themes owning the full lifecycle from research to offline/online experiments to technology transfer.
• Train and deploy end-to-end AV 2.0 models on our global fleet, using large-scale, diverse data to validate capabilities and improve generalisation across vehicles, markets, and driving conditions.
• Build high-value open-loop and closed-loop evaluations for core capabilities and representation learning.
• Align priorities and learn from the organisation - with AV Core, Evaluation, and Product Engineering on roadmaps and failure modes; from fleet, simulation, and product feedback; and through mentoring others on the team.
• Maintain awareness of the wider business context - division and company priorities, near-term product programmes, and how Core Model Safety work enables them.

About you

In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.

Essential

• 5+ years in ML engineering, including pathfinding in ambiguous problems - from scoping and evals to establishing a direction (and knowledge transfer) for others to build on.
• 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.
• Hands-on experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent.
• Hands-on experience training shared representations with multiple tasks or objectives (multi-stage or joint training), including real trade-offs across data and losses.
• Staff-level technical leadership: research-literate and pragmatic, setting direction, raising the bar, and leading cross-functional work without formal line management.

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 in reward modelling, behaviour modelling, model introspection, and/or interpretability.
• Experience with redundant or fallback architectures, safety-critical systems.
• Experience across foundations/pretraining and applied engineering teams; large-scale training infrastructure and/or agentic workflows.