Senior AI Data Pipeline Engineer (Autonomous Driving)

Responsibilities

• Develop high scale, reliable data extraction pipeline to extract millions of raw data from data collection fleet and convert to high-value scene data
• Develop data labeling pipelines to perform the auto labeling inferences for autonomous driving algorithms
• Develop advanced autonomous driving data SDK, including scene data search, datasets preparation, dataset loading, etc.
• Build up the data lakehouse for autonomous driving scene dataset, including the sensor data, calibration data, as well as annotation data
• Dig into performance bottlenecks all along the data processing pipelines, from data processing latency, data search latency to Test Procedure (TP) coverage
• Bootstrap and maintain infrastructure for data platform components—data processing pipeline, database, data lakehouse and data serving
• Collaborate with cross-functional teams, including ML algorithm, ML application, and Cloud Infra to align data pipelines with overall autonomous driving system architecture

Qualifications

• Bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field
• Minimum of 7 years of experience in Data Engineering, DataOps or ML Platform roles
• Proficient in Python and solid experience in Python SDK development
• Solid hands-on experience with data pipeline job orchestration with Databricks Workflows or Apache Airflow, as well as integrating data pipelines with machine learning models
• Solid working experience in Databases (e.g., MongoDB, PostgreSQL, etc)
• Extensive experience with data technologies and architectures such as Data Warehouse (e.g., Hive) or Lakehouse (e.g., Delta Lake)
• Experience with Apache Spark or other big data computing engines
• Excellent leadership and communication skills, with a demonstrated ability to lead technical projects

Preferred Qualifications

• Experience with autonomous vehicle sensor data (e.g., LiDAR, camera, radar)
• Experience with ML model training lifecycle (e.g., data preparation, model training / validation / deployment, etc)
• Understanding of modern AI frameworks (e.g., PyTorch, TensorFlow etc.)
• Understanding data governance principles, data privacy regulations, and experience implementing security measures to protect data