Staff Machine Learning Engineer, AV Core

📋 Job Overview

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.

🏢 About Wayve

Wayve is an AI company building the future of autonomous driving. We are pioneers in end-to-end AI, training driving models that learn from human feedback and real-world experience. Our technology is deployed on a global fleet, enabling vehicles to navigate complex environments with human-like understanding and adaptability.

🎯 The Role

You will drive Core Model Safety roadmap themes owning the full lifecycle from research to offline/online experiments to technology transfer. You will 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. You will build high-value open-loop and closed-loop evaluations for core capabilities and representation learning. You will 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. You will maintain awareness of the wider business context - division and company priorities, near-term product programmes, and how Core Model Safety work enables them.

✅ 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.

📌 Required Qualifications

• 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 Experience

• 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.

🎁 Benefits

The reasonably estimated salary for this role ranges from $336,400 to $370,300, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.

🛂 Visa & Eligibility

No specific visa or eligibility information provided.