Data Management Engineer

• Stakeholder Coordination: Liaise with internal teams to define data collection requirements and priorities, maintaining clear documentation on collection needs.
• Data Collection: Perform on-site data recording using our systems and directly obtain datasets from key stakeholders or customers.
• Data Storage & Archiving: Manage and organize large datasets across various storage solutions, including Google Cloud (GCloud) and local Network Attached Storage (NAS), ensuring data is secure, accessible, and up-to-date.

• Lidar Pre-processing: Conduct initial alignment and pre-validation of raw Lidar point cloud data to ensure its quality and usability.
• Data Validation & Quality Assurance: Design, Develop and Perform rigorous validation processes on labeled data received from vendors to confirm it meets our standards before being integrated into training or validation sets.
• Dataset Management: Handle the strategic splitting of datasets for training and validation purposes. Identify and document errors or inconsistencies in current datasets and coordinate with labeling teams for corrections.
• Validation & Regression Framework: Add new datasets into our validation framework, which includes creating precise Areas of Interest (AOIs) and generating new baseline performance metrics.

• Labeling Coordination: Manage the end-to-end labeling process by sending data to our labeling partners, tracking their progress, and serving as the primary technical point of contact.
• Documentation: Own and update all labeling documentation, including defining new classes and clarifying labeling instructions to ensure data consistency.
• Metrics & Reporting: Continuously improve the metrics and reports used for our validation, performance and regression testing, adding new parameters as needed to enhance our evaluation framework.
• Automation & Efficiency: Run, maintain, and improve our pre-labeling pipeline to increase the efficiency of our data operations.
• Tool & Industry Research: Conduct industry research to identify and evaluate new tools and technologies that can make our labeling and data management processes more efficient.

• Bachelor’s degree in Computer Science, Engineering, or a related technical field.
• Proven experience in managing large-scale datasets and complex validation pipelines for machine learning and computer vision applications.
• Proficiency in scripting languages such as Python for automation and data manipulation.
• Familiarity with data labeling processes and managing multiple labeling vendors.
• Strong organizational skills with the ability to manage multiple projects and priorities simultaneously.
• Excellent communication skills, with experience coordinating between technical teams and external vendors.
• Meticulous attention to detail, especially in data validation and quality control.
• Ability to identify a gap in processes and define a proper process to bridge over the gap.

• Strong knowledge of C++ / RUST.
• DevOps or MLOps experience.
• Hands-on experience with Lidar data and point cloud processing.

Ouster