Senior MLOps Manager

NVIDIA is seeking a Senior MLOps Manager to lead data operations, ground truth (GT) production, and delivery for its autonomous driving stack.

In this role, you will work with world-class data engineering, data factory, outsource labeling, and MLE teams on programs that directly impact NVIDIA's autonomous driving stack. You will own the end-to-end GT operations lifecycle for WR/INDF, from requirements and planning through annotation, QA, and final delivery to model training and evaluation.

Responsibilities:

• Own the GT production and delivery pipeline, ensuring datasets are delivered on time, at the right quality, and at the right scale for model training and validation.
• Define GT requirements with Data Analyst and MLE teams, and translate them into clear labeling specs, QA specs, volumes, SLAs, and quality targets.
• Manage day-to-day GT operations across internal teams and external vendors, including task allocation, throughput tracking, and issue triage.
• Establish and monitor key operational metrics (throughput, quality, rework rate, audit pass rate, labeling cost), driving continuous improvement using data-driven insights.
• Partner with tooling and data infrastructure teams to improve labeling tools, QA workflows, and GT data pipelines, including opportunities to leverage AI agents to boost efficiency.
• Lead cross-functional reviews and post-mortems when GT issues are discovered, and drive root-cause analysis and corrective actions.

Requirements:

• BS degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).
• 10+ years of overall professional experience in data operations, labeling operations, ML data programs, or related fields, working with large-scale data or ML-driven products.
• 5+ years of direct leadership/management experience leading data operations, labeling teams, or cross-functional GT programs (people management and/or end-to-end program ownership with defined KPIs and SLAs).
• Proven track record owning end-to-end data or GT operations, with clear examples of how you improved quality, efficiency, and reliability.
• Strong communication and stakeholder management skills, with experience working across engineering, ML, product, and vendor teams.
• Solid analytical skills and comfort working with metrics and dashboards to drive decisions.

Preferred Qualifications:

• Experience leading GT or labeling operations for autonomous driving, robotics, or other complex ML domains.
• Hands-on familiarity with annotation tools, dataset management platforms, and data quality frameworks.
• Experience applying AI/agentic tooling to improve operations (e.g., auto-labeling, active learning, agent-assisted QA, or workflow automation).
• Prior experience managing external labeling vendors, including setting up SLAs, quality targets, and feedback loops.