Data Engineer, App SW

Overview

Join Wayve’s Germany-based Data Engineering and Machine Learning team to build scalable data pipelines powering model development and production ML workflows for autonomous driving. You’ll curate large-scale real-world, synthetic, and partner datasets, ensure data quality, and enable diverse scenario coverage. Collaborate with ML engineers, Data Corpus, and partners to deliver production-ready data systems that accelerate iteration and improve driving intelligence. This role offers hands-on impact in localising and advancing AV capabilities for the German market, with opportunities to shape data-centric solutions at scale.

Pay / Benefits

• Salary benchmarked annually
• Equity/ownership opportunity
• Relocation support and visa sponsorship
• Hybrid working with core hours
• Learning and development budgets
• Comprehensive benefits (health insurance, dental, parental leave)

Responsibilities

• Build and optimize scalable data pipelines for model development, evaluation, and production ML workflows
• Ingest, transform, and curate large-scale datasets into structured, model-ready formats aligned with standardized taxonomies
• Develop data quality checks, validation processes, and monitoring for raw and processed data
• Curate real-world and synthetic data to improve scenario diversity and feature development
• Improve pipeline performance and reliability to accelerate ML iteration
• Collaborate with ML engineers, Data Corpus, AI Platform, and external partners to integrate pipelines with production systems
• Design and deliver scalable pipelines transforming data from diverse sources into structured datasets
• Support data ingestion, quality assurance, transformation, curation, evaluation, and ML workflows

Key requirements

• Proven experience building and operating scalable data pipelines or distributed data processing systems in production
• Strong Python software engineering skills with maintainable, well-tested code
• Proficient in SQL and PySpark, with experience in warehouse/OLAP concepts and Spark
• Experience with modern data pipeline architectures and DAG-based systems (Airflow, Flyte, Ray or similar)
• Solid understanding of robotics and automated driving data concepts (sensor characteristics, timestamping, coordinate transformations, calibration, ego-motion signals)
• Understanding of ML development workflows (training data generation, evaluation datasets, scenario mining, model iteration)
• Excellent communication and collaboration skills for cross-functional teams

• Cross-functional collaboration
• Attention to data quality
• Problem solving and initiative
• Python
• SQL
• PySpark

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