1 час назад
Data Engineer, Application Software (Autonomous Driving)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Engineer, Application Software (Autonomous Driving) (Python/SQL/PySpark): Building scalable data pipelines that transform real-world, synthetic, and partner datasets into reliable, model-ready data for autonomous-driving machine learning with an accent on data ingestion, quality assurance, curation, and evaluation. Focus on designing distributed processing and orchestration systems, mining driving scenarios, and integrating production-scale data workflows with ML engineering teams.
Location: Hybrid role based in an office in Tokyo, Japan; core working hours with time split between the office and working from home.
Company
develops model-based autonomous-driving technology and intelligent driving features.
What you will do
- Build and improve scalable data pipelines for model development, evaluation, and production machine-learning workflows.
- Ingest, transform, and curate large-scale real-world, synthetic, and partner-provided datasets into structured, model-ready formats.
- Develop data-quality checks, validation processes, and monitoring for vehicle-platform and processed datasets.
- Mine and curate data to improve scenario diversity, coverage, and feature-specific development.
- Improve pipeline performance, reliability, and usability to accelerate ML iteration.
- Collaborate with ML engineers, Data Corpus, AI Platform, and external partners.
Requirements
- Production experience building and operating scalable data pipelines or distributed data-processing systems.
- Strong Python software-engineering skills and experience developing maintainable, reliable, and well-tested software.
- Proficiency in SQL and PySpark, including warehouse or OLAP concepts, window functions, and distributed processing with Spark.
- 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, timestamps, clock synchronization, coordinate transformations, calibration, GNSS/IMU, and vehicle odometry.
- Understanding of ML workflows, including training-data generation, evaluation datasets, scenario mining, and model iteration; 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 behavior labels, scene classification, event tagging, and semantic understanding.
Culture & Benefits
- Full-time employment with a hybrid working policy.
- Time is shared between offices and workshops and working from home.
- Core working hours provide flexibility to determine a schedule with the team.
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