Data Infrastructure / Quality Engineering

• 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

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., * 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

Preferred experience

• 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

The base pay will be dependent on your skills, work experience, location, and qualifications. This role may also be eligible for equity & benefits. ($140,000 - $ 200,000)

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.