Staff Machine Learning Engineer (Vision Models)

Join Wayve, a leading company in the field of autonomous driving. As a Staff Machine Learning Engineer, you will be responsible for building and improving computer vision and scene understanding models that measure the performance of the Wayve Driver. You will work in a high-impact senior team, have access to fleet-scale data, and collaborate with various teams across the company.

• Développer, entraîner et affiner les modèles de compréhension de la scène au centre de la mesure hors ligne de Wayve.
• Améliorer les performances des modèles sur différentes plateformes de véhicules, géographies et conditions de conduite.
• Définir la vérité de base et les critères de correction à travers une taxonomie de conduite complexe, et les transformer en benchmarks automatisés.
• Proficient in Python and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices and comfort with large-scale training
• 5+ years in ML engineering, including training and shipping deep learning models in production, with pathfinding in ambiguous modelling problems from scoping through to a direction others build on
• Experience adapting or fine-tuning large pretrained or foundation models, and training shared representations across 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
• Able to measure your own models: comfortable defining and reading the metrics that show whether a model is genuinely improving
• Hands-on experience training modern computer vision models, including transformer-based and multimodal or VLM architectures for detection, segmentation, classification, or scene understanding, on camera and/or lidar sensor data
• Experience in 3D scene understanding and representation learning for geometric and semantic perception, including large-scale semantic enrichment of driving scenes
• Experience with offboard or offline modelling: auto-labelling, model distillation, temporal or world models, or other ways of exploiting compute that on-vehicle systems cannot
• Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems
• Experience with fleet-scale data and large-scale distributed training infrastructure
• We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply