Machine Learning Engineer, Application Software

Job description

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Engineering Teams The Application Engineering (App SW) team sits within Product & Delivery and works on critical initiatives that push the frontier of model-based autonomous driving. That covers core driving performance as well as feature-level intelligence such as personalisation, comfort and collaboration. 🧠 Your day-to-day As an ML Engineer, you’ll design and deliver ML-driven behaviours that scale from assisted to autonomous driving. Your work spans model architecture, data pipelines, evaluation frameworks and real-world deployment. You’ll work closely with the AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable and ready for production. 🧩 What you’ll be working on: - Developing and improving end-to-end driving models with state-of-the-art performance, robustness and generalisation. - Driving projects on personalised and collaborative driving, including behaviour conditioning, comfort tuning and user alignment. - Building evaluation pipelines and metrics for closed-loop and open-loop driving performance and product readiness. - Curating and mining real-world and synthetic data to drive scenario diversity, coverage and feature-specific development. - Influencing architecture choices, training methodologies and deployment pathways for production-scale learning systems. - Collaborating across teams to keep integration smooth and iteration fast. - Mentoring engineers and helping shape the long-term technical direction across Autonomy. 🙌 You should apply if: Essential: - Extensive and proven track record of shipping deep learning systems to production. - Expert in deep learning (esp. sequential models, control, planning, or perception). - 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. - Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components. - Ability to lead technical initiatives across teams, drive alignment, and mentor engineers. Desirable: - Prior work in autonomous driving, imitation learning, or trajectory prediction. - Familiarity with personalization, human behavior modeling, or driver intent inference. - Experience integrating ML systems into production hardware or multi-agent simulation. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. 🔍 The Interview Process: Our process is clear and respectful of your time: - Initial call / recruiter screen (30 mins) - Competency Interviews (Python programming, PyTorch debugging ; 2 hours t