Sr. Data Infrastructure & Quality Engineer

Ouster; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Ouster in San Francisco seeks a Sr. Data Infrastructure / Quality Engineer to architect, build, and validate cloud data pipelines powering autonomous sensing products.

You will transform raw LiDAR and multimodal streams into robust, reproducible training datasets for AI pipelines. You will own data infrastructure from prototype to production, building lakehouse/warehouse solutions, feature stores, and governance frameworks, while partnering with SW and ML teams to ensure data quality and

Qualifications

• 8+ years designing, building, validating scalable cloud infra and data pipelines.
• Enterprise-grade data systems handling massive unstructured data at scale.
• Strong knowledge of modern data infra, cloud platforms, and data quality checks.
• Proven ability to scale data platforms from prototype to production and maintain stability.

Responsibilities

• Design and develop robust cloud infrastructure, storage systems, and test frameworks for AI data pipelines.
• Own data infrastructure from concept to production-scale release.
• Architect data lakehouse/warehouse systems with data governance and lineage.
• Collaborate with SW and ML engineers on ingestion, versioning, and cloud orchestration.
• Develop automated data validation, ETL pipelines, IaC, and regression tests.
• Support deployments, iteration, and root-cause analysis for data issues.
• Assist data-tooling integration and automated labeling workflows.
• Contribute to pilot deployments and telemetry-driven design improvements.

Skills

Cloud infrastructure design

Data pipeline development

Data quality validation

IaC (Infrastructure as Code)

Pipeline monitoring

Tools

Cloud platforms (AWS/Azure/GCP)

Data lakehouse platforms

Feature stores

ETL tooling

Job description

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.

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