Data Engineer (Autonomous Driving)

Location: Tokyo, Japan. Hybrid working with in-person collaboration in dedicated offices and remote work; core hours apply. Relocation support and visa sponsorship are available where applicable.

Company

hirify.global develops an end-to-end AI platform for autonomous driving, enabling vehicles to learn from real-world experience and adapt across vehicle platforms and environments.

What you will do

• Build and improve scalable data pipelines supporting 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 datasets.
• Mine and curate data to improve scenario diversity, coverage, and feature-specific development.
• Improve pipeline performance, reliability, and usability while reducing bottlenecks in ML iteration.
• Collaborate with ML engineers, Data Corpus, AI Platform, customer programmes, and external partners.

Requirements

• Production experience building and operating 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 distributed processing.
• Experience with workflow orchestration and DAG-based systems such as Airflow, Flyte, Ray, or similar tools.
• Understanding of robotics and automated-driving data, including sensors, timestamping, clock synchronisation, coordinate transformations, calibration, GNSS/IMU, and vehicle odometry.
• Understanding of ML development workflows, training-data generation, evaluation datasets, scenario mining, and model iteration, with strong communication and collaboration skills.

Nice to have

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

Culture & Benefits

• Hybrid working with core hours and opportunities to work hands-on in vehicle workshops and labs.
• Market-benchmarked salaries, meaningful equity, and location-dependent benefits.
• Learning and development budgets supporting training, conferences, and professional growth.
• Comprehensive health and dental insurance, enhanced maternity and paternity leave, retirement or pension support where applicable, and wellbeing resources.
• A developing environment offering substantial ownership as processes and ways of working are built.

Hiring process

• Initial recruiter call lasting approximately 30 minutes.
• Competency interviews covering system design and data engineering, followed by technical interviews on Python coding and a technical domain.
• Final one-hour interview focused on mission and values alignment.