Senior Machine Learning Engineer, ADAS

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 ADAS Team Wayve's ADAS engineering teams build the perception and intelligence that power driver assistance in real-world driving. We work end-to-end, from creating high-quality training data, to developing and evaluating CV/3D perception models, to iterating quickly based on performance gaps. The team mixes "online" (on-car, latency/compute constrained) and "offline" (heavier, large-scale data generation) work, with a strong focus on measurable impact and shipping. 🧠 Your day-to-day - Train, debug, and improve computer vision and 3D perception models, iterating based on clear evaluation signals - Work across the full ML lifecycle: data, training, evaluation, and iteration - Partner with the team to decide what to tackle next, based on where the system is underperforming - Build scalable data pipelines, including auto-labelling and pseudo-labelling, to accelerate model development 🧩 What you'll be working on: - Core ADAS perception capabilities such as detection, classification, and instance segmentation, spanning lanes, objects, traffic signs, and traffic lights - End-to-end approaches that let the model act on the world directly, rather than relying on a chain of hand-coded intermediate steps (lane assist without ever explicitly detecting lane lines is the kind of outcome we're after) - Online perception that runs fast in-car as part of our production stack - Offline perception work using additional inputs not viable for on-road production, including extra sensors like lidar, future frames, maps, and extra compute, to improve data quality and coverage at scale - Building the "data flywheel": using offline models and 3D tracking/reconstruction to auto-generate labels and accelerate iteration across perception tasks 👤 You should apply if: - Built and shipped CV-focused deep learning systems, with strong applied ML engineering rather than research-only work - Experience with 3D perception concepts or pipelines, such as LiDAR, multi-view geometry, tracking, or 3D reconstruction - Comfortable owning work end-to-end, including evaluation and large-scale dataset generation - Enjoy pragmatic problem-solving under real product constraints - Excited to improve real-world driving performance through better perception - Automotive domain experience is a bonus, but not a blocker if your perception background comes from elsewhere 🌱 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 (programming and system design; 2 hours) -