Data Engineer

EngineersOfAI; Leonberg; Hybrid; EUR 70.000 - 100.000; Vollzeit

Zusammenfassung

Wayve in Germany seeks a Data Engineer within the Machine Learning–Application Software team to build scalable data pipelines for model-based autonomous driving, data ingestion, quality checks, and production-ready datasets.

You will work with Data Corpus, ML engineers, and external partners to ensure pipelines meet performance, reliability, and ML workflow needs.

Qualifikationen

• 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.

Aufgaben

• Build and deliver scalable data pipelines supporting 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 formats.
• Develop data quality checks, validation processes, and monitoring for high data quality and traceability.
• Curate and mine real-world and synthetic data to drive scenario diversity and feature development.
• Improve pipeline performance, reliability, and usability to accelerate ML iterations.
• Collaborate with ML engineers, Data Corpus, and AI Platform to integrate data pipelines with production learning systems.

Kenntnisse

Python

SQL

PySpark

Airflow

Spark

Data pipelines

Data quality checks

Communication

Tools

Airflow

Spark

Python

Jobbeschreibung

About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

Make Wayve the experience that defines your career!

The Role

As a Data Engineer within the Machine Learning team in Application Software, you’ll contribute to critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalization, comfort, and collaboration.

You’ll design and deliver scalable data pipelines that transform vast amounts of data from diverse internal and external sources into structured, reliable, and model-ready datasets. Your work will span data ingestion, data quality assurance, transformation, curation, evaluation and ML support. You’ll collaborate deeply with Wayve’s Data Corpus teams and ML engineers to build systems that are performant, adaptable, and ready for production.

Key Responsibilities:

• 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, and model-ready formats aligned with standardised taxonomies and coordinate systems.


• Develop data quality checks, validation processes, and monitoring to ensure both raw data from our vehicle platforms 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, helping reduce bottlenecks and increase iteration velocity across ML development.


• Collaborate closely with Machine Learning engineers, Data Corpus, AI Platform, and external partners to ensure data pipelines integrate effectively with production-scale learning systems.

About You

In order to set you up for success as a Data Engineer at Wayve, we’re looking for the following skills and experience.

Essential

• 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.

Desirable