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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The company is pioneering a mapless, hardware-agnostic approach that enables vehicles to learn, adapt, and improve at scale.
As a Data Engineer within the Machine Learning team (part of Product & Delivery), you will design and deliver scalable data pipelines that transform vast amounts of data from diverse internal and external sources into structured, reliable, model-ready datasets. Your work will span data ingestion, quality assurance, transformation, curation, evaluation, and ML support.
Key responsibilities include:
- Building and improving scalable data pipelines supporting model development, evaluation, and production ML workflows for autonomous driving
- Ingesting, transforming, and curating large-scale real-world, synthetic, and partner-provided datasets into structured formats aligned with standardized taxonomies and coordinate systems
- Developing data quality checks, validation processes, and monitoring to ensure raw vehicle data and processed datasets are high-quality, complete, consistent, traceable, and fit for ML use cases
- Curating and mining real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development
- Improving pipeline performance, reliability, and usability to reduce bottlenecks and increase iteration velocity across ML development
- Collaborating closely with ML engineers, Data Corpus teams, AI Platform, and external partners to ensure data pipelines integrate effectively with production-scale learning systems
Wayve operates a hybrid working model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company offers competitive salaries benchmarked against the market, meaningful equity, relocation support and visa sponsorship where applicable, learning and development budgets, and comprehensive benefits including health insurance, dental, enhanced parental leave, retirement/pension, and wellbeing partnerships.
REQUIREMENTS:
Essential:
- Proven experience building and operating scalable data pipelines or distributed data processing systems in production environments
- Strong software engineering skills in Python with solid foundation in maintainable, reliable, and well-tested software development practices
- 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 such as Airflow, Flyte, Ray, or similar
- Solid understanding of robotics and autonomous driving data concepts, including sensor characteristics, timestamping and clock synchronization, coordinate transformations, calibration, and ego-motion signals such as GNSS/IMU and vehicle odometry
- Understanding of machine learning development workflows, including training data generation, evaluation datasets, scenario mining, and model iteration
- Excellent communication and collaborative skills, capable of working effectively 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
