About this role
About Turing: Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in software engineering, logical reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L.
About the Role: We are seeking an engineer responsible for designing, implementing, and maintaining data-validation workflows inside Docker-based build pipelines. This role involves creating and managing Dockerfile labels, metadata standards, and validation scripts that ensure datasets, schemas, and model artifacts meet quality and compliance requirements before deployment.
You will work closely with data engineering, machine learning, and DevOps teams to build reliable, reproducible, and fully validated containerized data pipelines.
What does day-to-day look like
- Develop and optimize Dockerfiles with built-in data-validation steps.
- Implement LABEL metadata for dataset versions, schemas, and lineage.
- Create validation scripts (Python/Bash) for schema checks, data integrity, and quality control.
- Integrate validation steps into CI/CD pipelines and enforce fail-on-bad-data checks.
- Document standards for Dockerfile labeling, validation logic, and data governance.
Required Skills
- Experienced DevOps engineers with 4+ years of experience