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Lead ownership of the vehicle sensing subsystem: define sensing requirements, drive sensor architecture and integration, build validation frameworks, set benchmarks and acceptance criteria, diagnose sensor issues, and partner with ML, Hardware, Vehicle SW and Product to ensure reliable on-road performance.

Design and lead the embedded software architecture for Wayve's autonomy platform. Develop prototypes and reference implementations, optimize system performance and reliability, collaborate with AI, platform, and vehicle teams and external partners (NVIDIA, Qualcomm, OEMs), drive modern development practices for high-assurance embedded software, mentor senior engineers, and represent Wayve in external technical discussions.

Deliver production-ready ML model releases: train and iterate PyTorch models, evaluate and debug performance, apply optimizations (quantisation, distillation), collaborate with performance and deployment teams, and ensure models meet tight on-vehicle runtime constraints.

Define and manage technical architecture for integrating Wayve's autonomous driving software with OEM/Tier1 vehicle systems. Translate product and customer needs into interface contracts, ownership boundaries, integration specifications, test requirements, and program-facing documentation. Coordinate across Product, Software Architecture, Validation, and customer teams and represent Wayve in OEM/Tier1 technical discussions to identify risks, dependencies, and validation criteria.

The Machine Learning Engineer role at Wayve focuses on leading initiatives in model-based autonomous driving, involving the development and delivery of ML-driven behaviors, collaboration across teams, and mentoring other engineers.

Lead and grow a TPM function for the Data org, owning delivery of data platform, ingestion, pipelines, enrichment, labeling, and dataset quality. Drive flagship programs, establish scalable governance and KPIs, coordinate cross-functional dependencies, and partner with engineering to improve data quality, throughput, cost, and time-to-insight.

Develop, train, debug, and improve computer vision and 3D perception models for ADAS. Own end-to-end ML lifecycle work (data, training, evaluation, iteration), build scalable data pipelines including auto-/pseudo-labeling, and contribute to online (in-car) and offline perception systems for detection, segmentation, tracking and 3D reconstruction.

Analyze real-world autonomous vehicle run data to identify root causes, produce top-issue reports, validate triage automation, and collaborate with Detective and Triage engineers to drive issue resolution, documentation, and process improvements to maintain ADAS safety and reliability.

As a Product Feature Owner, you will oversee ADAS features from concept to production, collaborating with OEMs and engineering teams to align features with customer needs and ensure compliance with industry standards.

Wayve is expanding its German office and invites interested candidates to register for future job opportunities. Seeking individuals passionate about self-driving cars.