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.
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
Qualifications
- 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)
We acknowledge the confidence gap at Ouster. You do not need to meet all of these requirements to be the ideal candidate for this role.
