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Wayve

End-to-end autonomous-driving software for vehicles

Data Engineer - Application Software

Data EngineeringUpdated on Nov 9
Full-timeTokyo, Japan

About the Job

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.
  • Proficiency in SQL and PySpark, with experience using warehouse and 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.
  • A 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.
  • An understanding of machine learning development workflows, including training data generation, evaluation datasets, scenario mining, and model iteration.
  • The ability to work effectively with interdisciplinary teams.

Responsibilities

  • Design and deliver scalable data pipelines that turn diverse internal and external sources into structured, reliable, model-ready datasets, spanning data ingestion, data quality assurance, transformation, curation, evaluation, and machine learning support.
  • Work with Data Corpus teams, customer programmes, and machine learning engineers, bringing up ingestion pipelines for new vehicle platforms and sensor configurations and tackling high-impact bottlenecks first.
  • Build and improve scalable data pipelines supporting model development, evaluation, and production machine learning 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 raw vehicle data and processed datasets are high-quality, complete, consistent, traceable, and fit for machine learning 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 machine learning development.
  • Collaborate with machine learning engineers, Data Corpus, AI Platform, and external partners so data pipelines integrate effectively with production-scale learning systems.

Desired Qualifications

  • 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, and semantic understanding.

About the Company

Wayve

End-to-end autonomous-driving software for vehicles

Wayve provides end-to-end autonomous-driving software for automakers and fleets that runs entirely on onboard compute and sensors, without relying on HD maps or city-by-city engineering. The system uses foundation models capable of zero-shot driving and has demonstrated ability to drive across 500+ cities in Europe, North America, and Japan. Unlike traditional AV stacks that depend on mapped data and per-city customization, Wayve aims to generalize driving with its onboard AI across diverse environments. Its goal is to scale commercial robotaxi trials from 2026 and bring supervised autonomy to consumer vehicles from 2027, backed by substantial funding partnerships to support rapid deployment.

Company size
1,001-5,000
Company stage
Series D
Total funding
$2.6B
Headquarters
London, United Kingdom
Founded
2017

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