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# Data Infrastructure / Quality Engineering

- **Company:** Ouster, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $140,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Cloud Computing, Cloud Engineering, Data Validation, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Regression Testing, Unstructured Data, Core Data, Integration Tests, Data Lineage, Machine Learning Operations, Software Version Control, Data Pipelines
- **Published:** August 3, 2026
- **Apply:** https://www.dice.com/job-detail/02652154-2fdd-4392-be4c-addcc4b1bc0f

## About the Role

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)

## Description

* 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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