Data Engineer, Application Software

The Machine Learning team within Application Software, part of Product & Delivery, works on initiatives that push the frontier of model-based autonomous driving. As a Data Engineer, you will design and deliver scalable data pipelines that turn vast amounts of data into structured, reliable and model-ready datasets for autonomous driving, working closely with Wayve's Data Corpus teams, customer programmes and ML engineers.

Responsibilities

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; Work across data ingestion, data quality assurance, transformation, curation, evaluation and ML support; Work closely with Wayve's Data Corpus teams, customer programmes and ML engineers; Bring up ingestion pipelines for new vehicle platforms and sensor configurations, and tackle the highest-impact bottlenecks first; Build and improve scalable data pipelines that support model development, evaluation and production ML workflows for autonomous driving; Ingest, transform and curate large-scale real-world, synthetic and partner-provided datasets into structured, reliable, model-ready formats aligned with standardised taxonomies and coordinate systems; Develop 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; Curate and mine real-world and synthetic data to drive scenario diversity, coverage and feature-specific development; Improve pipeline performance, reliability and usability, reducing bottlenecks and increasing iteration velocity across ML development; Collaborate closely with ML engineers, Data Corpus, AI Platform and external partners so data pipelines integrate effectively with production-scale learning systems

Requirements

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

Nice to have

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

Benefits

Salaries benchmarked against the market annually; Meaningful equity; 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