4 months ago
Sunnyvale, CA, USASenior
Base Salary
$133k - $254k/yr
Responsibilities
- Develop reliable, high-scale data extraction pipelines that convert fleet-collected raw data into high-value autonomous-driving scene data.
- Build data-labeling pipelines that perform auto-labeling inferences for autonomous-driving algorithms.
- Develop data SDKs for scene search, dataset preparation, and dataset loading.
- Build and maintain the autonomous-driving data lakehouse containing sensor, calibration, and annotation data.
- Analyze and improve data-processing latency, data-search latency, and test-procedure coverage.
- Maintain infrastructure for data-processing pipelines, databases, lakehouse systems, and data serving.
- Collaborate with ML algorithm, ML application, and cloud infrastructure teams on autonomous-driving system architecture.
- Lead technical projects and align data-platform development with the ML development lifecycle.
Requirements
- Bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field.
- At least 7 years of experience in Data Engineering, DataOps, or ML Platform roles.
- Proficiency in Python and substantial experience developing Python SDKs.
- Hands-on experience orchestrating data-pipeline jobs with Databricks Workflows or Apache Airflow and integrating pipelines with machine-learning models.
- Working experience with databases such as MongoDB and PostgreSQL.
- Extensive experience with data architectures and technologies such as Hive data warehouses or Delta Lake lakehouses.
- Experience with Apache Spark or other big-data computing engines.
- Strong leadership and communication skills, including the ability to lead technical projects.
- Preferred: experience with autonomous-vehicle sensor data including LiDAR, cameras, or radar.
- Preferred: experience with ML model-training lifecycles, including data preparation, training, validation, and deployment.
- Preferred: understanding of PyTorch, TensorFlow, data-governance principles, data-privacy regulations, and data-security implementation.
Tech Stack
Categories
Data Engineering
