Wayve - Data Engineer, Application Software

• As a Data Engineer, you’ll design and deliver scalable data pipelines that turn vast amounts of data from diverse internal and external sources into structured, reliable and model-ready datasets. Your work spans data ingestion, data quality assurance, transformation, curation, evaluation and ML support. You’ll work closely with Wayve’s Data Corpus teams, customer programmes and ML engineers, bringing up ingestion pipelines for new vehicle platforms and sensor configurations, and tackling the highest-impact bottlenecks first. • 🧩 What you’ll be working on: • Building and improving scalable data pipelines that support 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, reliable, model-ready formats aligned with standardised taxonomies and coordinate systems. • Developing data quality checks, validation processes and monitoring so that 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, reducing bottlenecks and increasing iteration velocity across ML development. • Collaborating closely with ML engineers, Data Corpus, AI Platform and external partners so data pipelines integrate effectively with production-scale learning systems. • 🙌 You should apply if: • Proven experience building and operating scalable data pipelines or distributed data processing systems in production environments. • Strong software engineering skills in Python, with a solid foundation in maintainable, reliable, and well-tested software development practices. • Proficient 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 automated driving data concepts, including sensor characteristics, timestamping and clock synchronisation, 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. • Prior work in calibration, perception, imitation learning, or trajectory prediction. • Experience with third-party dataset ingestion and transformation. • Familiarity with automated driving data and scenario taxonomies, including ODD definitions, manoeuvre and behavior labels, scene classification, event tagging, semantic understanding. • 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.