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Wayve is building an AI platform for autonomous driving using end-to-end learning. The company is establishing a Germany-based team to localize and advance autonomous driving technology for the German market.
In this role, you will build and operate scalable data pipelines that support machine learning model development, evaluation, and production workflows for autonomous driving systems. Your responsibilities include:
- Designing and implementing scalable data pipelines that ingest, transform, and curate large-scale real-world, synthetic, and partner-provided datasets into structured, model-ready formats aligned with standardized taxonomies and coordinate systems.
- Developing data quality checks, validation processes, and monitoring systems to ensure raw vehicle platform data and processed datasets are high-quality, complete, consistent, traceable, and suitable for ML use cases.
- Mining real-world and synthetic data to drive scenario diversity and coverage for feature-specific development.
- Optimizing pipeline performance and reliability to reduce bottlenecks and increase iteration velocity across ML development teams.
- Collaborating closely with Machine Learning engineers, Data Corpus teams, AI Platform engineers, and external partners to integrate data pipelines with production-scale learning systems.
- Contributing to critical initiatives that advance both core driving performance and feature-level intelligence such as personalization, comfort, and collaboration.
You'll work in a hybrid model with core hours in the Leonberg office, with opportunities to engage hands-on in vehicle workshops and labs. Wayve operates main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg.
REQUIREMENTS:
Essential:
- Proven experience building and operating scalable data pipelines or distributed data processing systems in production environments.
- Strong Python software engineering skills with demonstrated ability to write maintainable, reliable, and well-tested code.
- Proficiency in SQL and PySpark, with experience using warehouse/OLAP concepts, window functions, and Spark for distributed data processing.
- Experience with modern data pipeline architectures, including workflow orchestration and DAG-based systems (Airflow, Flyte, Ray, or similar).
- Solid understanding of robotics and autonomous driving data concepts: sensor characteristics, timestamping and clock synchronization, coordinate transformations, calibration, ego-motion signals (GNSS/IMU, vehicle odometry).
- Understanding of machine learning development workflows, including training data generation, evaluation datasets, scenario mining, and model iteration.
- Excellent communication and collaborative skills for working with interdisciplinary teams.
Desirable:
- Prior work in calibration, perception, imitation learning, or trajectory prediction.
- Experience with third-party dataset ingestion and transformation.
- Familiarity with autonomous driving data and scenario taxonomies, including ODD definitions, maneuver and behavior labels, scene classification, event tagging, semantic understanding.
