Machine Learning Engineer, ADAS

Overview

In this role you will advance Wayve’s ADAS perception capabilities by training and refining CV/3D models. You will work end-to-end across data, training, evaluation and iteration, collaborating with cross-functional teams to improve in-car performance. You will build scalable data pipelines and labeling workflows to accelerate model development. The role combines online (in-car) and offline data-centric work to ship measurable improvements. You contribute to a mission-driven AI platform that aims for safe, mapless autonomy across diverse vehicles.

Pay / Benefits

• equity
• relocation support and visa sponsorship
• hybrid working
• learning and development budget
• comprehensive health and wellbeing benefits

Responsibilities

• Train, debug and improve CV/3D perception models across lanes, objects, signs and lights
• Own end-to-end ML lifecycle: data, training, evaluation, iteration
• Build scalable data pipelines and labeling workflows (auto-labelling / pseudo-labelling)
• Develop offline pipelines (tracking, 3D reconstruction) to generate labeled data over time
• Contribute to online models that run fast in-car or offline models that improve data quality at scale

Key requirements

• Experience shipping CV-focused deep learning systems
• Knowledge of 3D perception concepts or pipelines (LiDAR, multi-view geometry, tracking, 3D reconstruction)
• Ability to own work end-to-end, including evaluation and dataset generation
• Pragmatic problem-solving under real product constraints
• Interest in improving real-world driving performance through perception

• pragmatic problem-solving
• ownership and accountability
• collaboration across cross-functional teams
• CV-focused deep learning
• 3D perception concepts (LiDAR, tracking, 3D reconstruction)
• data pipelines and dataset generation

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