Machine Learning Engineer, ADAS

Das ist der Job

Qualifications Proven experience building and shipping computer‑vision‑focused deep‑learning systems.

Darum lohnt es sich

Interview Process Initial call / recruiter screen (30 min) Competency interviews (programming and system design, 2 hrs total) Deep‑dive technical interviews (domain‑specific, 1 hr total) Final interview: mission & values alignment (1 hr) Benefits Salaries benchmarked against the market, reviewed annually Meaningful equity, sharing in the ownership and long‑term success of Wayve Relocation support and visa sponsorship where applicable Hybrid working, core hours, and the chance to work hands‑on in vehicle workshops and labs Learning and development budgets with support for training, conferences and growth Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more EEO Statement Wayve is committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

Responsibilities Train, debug, and improve computer vision and 3D perception models, iterating based on clear evaluation signals. Work across the full machine‑learning lifecycle, from data collection and annotation to training, evaluation and deployment.

Build and maintain scalable data pipelines, including auto‑labelling and pseudo‑labelling, to accelerate model development. Deliver core ADAS perception capabilities such as detection, classification, and instance segmentation for lanes, objects, traffic signs, and traffic lights.

Contribute to offline pipelines—tracking and 3D reconstruction—to generate large labelled datasets and back‑propagate high‑quality labels through time. Own work end‑to‑end, including evaluation and dataset generation, under real product constraints, improving real‑world driving performance.

Strong applied machine‑learning engineering skills, with a track record of delivering production models. Experience with 3D perception concepts or pipelines (e.g., LiDAR, multi‑view geometry, tracking, 3D reconstruction). Comfortable owning work from start to finish—evaluation, dataset creation, and deployment.

Pragmatic problem‑solving mindset, working effectively under real product constraints.

Wayve will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews, but we do capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help identify areas of improvement in our hiring process. #J-18808-Ljbffr