Sr. Data Infrastructure & Quality Engineer

Ouster•San Francisco, CA

3d•$140,000 - $200,000

About The Position

At Ouster, we are pioneering the future of Physical AI. Our advanced vision algorithms and cutting-edge sensor hardware power the next generation of autonomous systems - from robots to smart infrastructure - building a safer and more efficient world. The Sr. Data Infrastructure / Quality Engineering role will play a crucial part in architecting, building, and validating the cloud infrastructure and data loops that power our products for autonomy and physical AI customers from the ground up. You will build the foundation for scalable pipelines capable of handling the massive and chaotic nature of LiDAR data, along with multimodal field data ranging from raw multi-camera streams to GNSS/RTK, IMU, and vehicle odometry. The Industrial Autonomy team is looking for a self-starter who can independently drive complex data systems from conception to completion with a high degree of autonomy, transforming raw, multi-sensor streams into robust, reproducible training datasets for our AI pipelines.

Requirements

8+ years of experience designing, building, and validating scalable cloud infrastructure and data pipelines
Experience building and testing data systems to enterprise-grade standards capable of processing massive, unstructured datasets at a production scale
Extensive knowledge of modern data infrastructure, cloud platforms, and data quality validation frameworks
Experience ramping at least one core data platform from initial prototype to production release + supporting its long-term stability

Nice To Haves

Direct experience with autonomy, robotics, industrial equipment, or automotive data loops, specifically handling massive streams of multimodal vehicle telemetry and sensor data
Experience building and validating active learning pipelines, continuous training infrastructure, and automated data curation systems
Experience with data governance, safety-critical data validation frameworks, or compliance standards for autonomous systems
Experience deploying and optimizing high-performance GPU cloud inference services, with specific expertise utilizing the NVIDIA architecture (e.g., Triton)
Experience collaborating with data labeling services, including internal labeling, third-party labeling vendors, and integrating external annotation services

Responsibilities

Design and develop robust cloud infrastructure, storage systems, and automated testing frameworks for AI training datasets and machine learning pipelines
Own data infrastructure from concept through prototype architecture, data quality validation, and production-scale release
Experience architecting and validating data lakehouse/warehouse systems, feature stores, and automated data governance frameworks to ensure data lineage, security, and reproducible training datasets
Partner with SW and ML engineers to build and optimize sensor data ingestion, model/data/label versioning systems, cloud orchestration, and high-throughput pipeline architectures
Develop automated data validation scripts, core ETL pipelines, infrastructure-as-code (IaC), and comprehensive regression testing suites
Support pipeline deployments, continuous architectural iteration, and root cause analysis for data corruption, pipeline bottlenecks, or infrastructure failures
Support data-tooling integration, automated data labeling workflows, and third-party vendor integration testing
Contribute to pilot data deployments and field telemetry loops, incorporating learnings into future architectural designs