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Ouster

Robot type
Sensors and Components
Location
San FranciscoCaliforniaUSA
Job type
Software
Posted
Jul 29, 2026
Salary
$140,000–$200,000 a year
Full-time

Sr. Data Infrastructure & Quality Engineer

Job description

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,…

Job 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
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