Machine Learning Engineer

Job description

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 an ML Engineer within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalisation, comfort, and collaboration. You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production. Key Responsibilities: Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization. Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment. Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness. Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development. Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems. Collaborate cross-functionally across various teams to ensure integration and iteration velocity. Mentor senior engineers and shape the…