Machine Learning Engineering Manager, ADAS

In this role, you will lead a growing ADAS ML engineering team and drive the end-to-end AI driving approach into production for a specific OEM program. You partner with product and TPMs to shape priorities, while coaching engineers to grow as the team scales. You balance hands-on technical leadership with management to deliver embedded AI models that run efficiently on constrained hardware. This is a high-impact opportunity to help Wayve deploy safe, scalable autonomous capabilities across vehicles.

• Salaries benchmarked against market
• Meaningful equity
• Relocation support and visa sponsorship
• Hybrid working with core hours
• Learning and development budgets
• Comprehensive benefits: health, dental, maternity/paternity leave, retirement and wellbeing discounts

• Lead a ~7-person ML engineering team (mix of new hires and existing members)
• Act as tech lead across multiple workstreams, setting problem-solving vision and dependencies
• Review and approve delivery plans for owned tasks
• Partner with TPMs and Product to shape priorities and roadmaps
• Coach and develop ML engineers, fostering career growth
• Translate end-to-end AI driving approach into production for OEM programs
• Deploy ML models from PoC to production on embedded automotive hardware
• Optimize runtime and inference for constrained hardware
• Build and scale the team during a high-priority growth phase
• Collaborate with software, embedded, and product teams across system boundaries to deliver a coherent product

• Expert background in ML, specifically computer vision
• People manager experience and enjoyment of developing engineers
• Experience working across software, embedded systems and product teams
• Hands-on experience with embedded runtime and inference optimisation
• Clear and thoughtful communication across sites and cultures
• Ability to stay steady and keep the team moving when requirements are unclear or change frequently

• Clear communication
• People leadership
• Cross-functional collaboration
• Computer vision in ML
• Embedded software and runtime/inference optimisation
• Productionisation of ML models on embedded hardware