Wayve · London, United Kingdom
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 a …truncated……
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