Senior Machine Learning Engineer, ADAS

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 TeamWayve'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-dayTrain, debug, and improve computer vision and 3D perception models, iterating based on clear evaluation signalsWork across the full ML lifecycle: data, training, evaluation, and iterationPartner with the team to decide what to tackle next, based on where the system is underperformingBuild 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 lightsEnd-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 stackOffline 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 scaleBuilding the "data flywheel": using offline models and 3D tracking/reconstruction to auto-generate la