Data Engineer (Autonomous Driving)

Location: Leonberg, Germany; hybrid working model with in-person collaboration and remote work

Company

hirify.global builds an AI platform for autonomous driving, using real-world learning to develop adaptable driving intelligence for vehicles and OEM partners.

What you will do

• Build and operate scalable data pipelines for model development, evaluation, and production machine learning workflows.
• Ingest, transform, and curate real-world, synthetic, and partner-provided datasets into structured, model-ready formats.
• Develop data quality checks, validation processes, and monitoring for vehicle and processed data.
• Mine and curate data to improve scenario diversity, coverage, and feature-specific development.
• Improve pipeline performance, reliability, and usability for faster machine learning iteration.
• Collaborate with Machine Learning, Data Corpus, AI Platform, and external partner teams.

Requirements

• Production experience building scalable data pipelines or distributed data processing systems.
• Strong Python software engineering skills with maintainable, reliable, and well-tested development practices.
• Proficiency in SQL and PySpark, including warehouse or OLAP concepts, window functions, and Spark-based distributed processing.
• Experience with workflow orchestration and DAG-based systems such as Airflow, Flyte, Ray, or similar.
• Understanding of robotics and autonomous driving data, including sensor characteristics, timestamping, clock synchronization, coordinate transformations, calibration, GNSS/IMU, and vehicle odometry.
• Understanding of machine learning data workflows, strong communication skills, and effective interdisciplinary collaboration.

Nice to have

• Experience in calibration, perception, imitation learning, or trajectory prediction.
• Experience ingesting and transforming third-party datasets.
• Familiarity with autonomous driving data and scenario taxonomies, including ODD definitions, maneuver and behavior labels, scene classification, event tagging, and semantic understanding.

Culture & Benefits

• Hybrid work with core hours and hands-on work in vehicle workshops and labs.
• Relocation support and visa sponsorship where applicable.
• Meaningful equity and market-benchmarked salaries.
• Learning and development budgets for training, conferences, and professional growth.
• Health insurance, dental coverage, enhanced parental leave, retirement or pension benefits where applicable, wellbeing support, and team socials.

Hiring process

• Initial recruiter call covering background, interests, and hiring context.
• Competency interview focused on system design and data engineering.
• Technical deep dives, followed by a final mission and values interview.