Data Engineer, App SW

In this role you will build and optimize data pipelines to support ML model development and production workflows for autonomous driving. You will ingest and curate large-scale datasets to model-ready formats aligned with standardized taxonomies, and implement data quality checks to ensure high-quality data for ML use cases. You’ll drive data curation and scenario diversity while collaborating with ML, Data Corpus, and AI Platform teams to enable scalable learning systems. This is a hands-on, impact-focused position as Wayve expands its German AV capabilities. You will contribute to core driving performance and feature-level intelligence while working in a cross-functional, mission-driven env

• hybrid working model
• equity participation
• relocation and visa where applicable
• health and dental benefits
• learning and development budgets
• wellbeing and family leave

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

• Proven experience building and operating scalable data pipelines in production
• Strong Python software engineering skills with maintainable and tested code
• Proficient in SQL and PySpark; experience with warehouse/OLAP concepts and Spark
• Experience with workflow orchestration 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 including training data generation, evaluation datasets, scenario mining and model iteration
• Excellent communication and collaboration skills with cross-functional teams

• Collaborative mindset
• Problem-solving orientation
• Clear communication
• Python
• SQL
• PySpark