Program Manager, Data Quality

Nuro•Mountain View, CA

About The Position

Nuro is seeking a Program Manager, Data Quality to join their ML Operations team. This role is crucial for ensuring the accuracy and reliability of the data used to train self-driving vehicles. The position involves investigating annotation faults, managing quality processes for offshore annotation teams, and acting as a liaison between Autonomy Engineering and ML Operations. The ideal candidate will have a strong understanding of ML data pipelines, SQL fluency, and a systems-level mindset to identify and fix structural quality issues. This role offers a unique opportunity to significantly impact Nuro's mission of making autonomy accessible and safe.

Requirements

5+ years embedded with ML, data operations, or software engineering teams.
SQL fluency.
Deep experience with ML data pipelines and labeling ecosystems: annotation workflows, quality sampling, taxonomy design, and inter-annotator agreement.
A systems-level mindset: identify where quality breaks down structurally and design the mechanism that fixes it.
Clear, confident communication: translate nuanced data quality findings into precise safety or business risks for senior leadership.
Experience managing large-scale offshore or globally distributed annotation teams.
Bachelor's degree in a technical or business discipline, or equivalent practical experience.

Nice To Haves

Background in autonomous vehicles, robotics, computer vision, or ML model training.
Prior experience in ML engineering, data engineering, or technical consulting.
A demonstrated track record of improving training data quality at scale, with metrics to show for it.

Responsibilities

Investigate annotation faults by querying databases directly to identify root causes.
Translate model accuracy regressions into specific labeling workflow problems and scope fixes.
Audit live pipelines to spot systematic edge-case misclassifications and build cases for restructuring labeling.
Manage the processes for quality management for the offshore annotation team.
Serve as the primary contact for quality diagnostics in ML Operations and for Engineering regarding labeling decisions' impact on model learning.
Define what 'good' looks like for each data type across active labeling pipelines and instrument pipelines to measure against it continuously.
Build inter-annotator agreement frameworks, taxonomy governance, and sampling methodologies for offshore production scale.
Design scalable processes to reduce systematic errors and support evolving ML training requirements.
Audit live workflows, query production databases, and trace accuracy failures to their structural root cause.
Apply statistical process control thinking to distinguish labeling errors from labeling system errors and drive corrective changes.
Connect ML labeling quality metrics directly to model performance and safety outcomes in partnership with Autonomy Engineering leadership.
Build executive-ready reporting that frames quality gaps as safety and business signals.
Drive alignment across engineering, product, and global ops with clear analysis and well-reasoned recommendations.