Senior ML Platform Engineer (Autonomous Driving)

At 42dot, our AD ML Platform Engineers build the core data platform and ML training / eval platform for the cutting edge algorithms in autonomous driving. We develop the distributed system of a scalable data platform for large-scale dataset (millions of scenes), as well as high-performance data serving SDKs for ML model training / evaluation.

The platforms we deliver could highly improve the efficiency of ML model development lifecycle, including training, evaluation, deployment, as well as monitoring in the cloud environment.

- Set technical strategy and oversee development of high scale, reliable data platform to manage, visualize and serve large-scale datasets for ML model training and validation.

- Build up the data lakehouse for autonomous driving scene datasets, including the sensor data, calibration data, as well as annotation data

- Drive the Autonomous Driving Data SDK development, including scene data search, datasets preparation, dataset loading, etc.

- 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 ML Platforms with overall Autonomous Driving System Architecture.

- Bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field.

- Minimum of 7 years of experience in Data Engineering or ML Platform roles

- Expert-level proficiency in Python and solid experience in Python SDK development

- Solid working experience in Databases (e.g., MongoDB, PostgreSQL, etc)

- Strong understanding of modern AI frameworks (e.g., PyTorch, TensorFlow etc.), especially the principle of distributed data loader for model training

- Hands-on experience with data pipeline job orchestration with Databricks Workflows or Apache Airflow, as well as integrating data pipelines with machine learning models

- 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

- 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 data governance principles, data privacy regulations, and experience implementing security measures to protect data