Senior Project Manager, Machine Learning Operations

Nuro•Mountain View, CA

1d•$120,270 - $180,410

About The Position

Nuro takes an ML-first approach to autonomous driving. The quality of our Nuro Driver™ is a direct function of the data that trains it, and this role owns that standard. As a Senior Project Manager, ML Operations, you are not an execution manager. The team already has operators focused on keeping pipelines running. What we need is someone who steps back, looks across all of them, and asks: where is quality breaking down, why, and what do we do about it? You will co-own more than 10 active data labeling pipelines alongside senior leadership, with a singular focus on data quality and gap closure. You will be the person who identifies the signal in the noise, connects dots across pipelines that execution-focused OPMs cannot see from their vantage point, and drives the systemic improvements that raise the accuracy of our training data. Data labeling at this scale is unforgiving. A single systematic flaw in how objects are annotated or how edge cases are classified can propagate silently through training and surface as a safety regression on the road. This role demands someone wired for that level of precision, someone for whom "close enough" is never acceptable and who can see the difference between a labeling error and a labeling system error.

Requirements

5+ years of project or program management experience embedded with ML, data operations, or software engineering teams. You have been close to the work, not managing from a distance.
Quality-obsessed, not just execution-obsessed. You are energized by finding what is broken, not just by shipping on time. You ask why accuracy is degrading before asking when the pipeline will recover.
Visionary and hands-on in equal measure. You can design the system-level quality framework and personally dig into a database to trace where a labeling fault originated. You do both without being asked.
Hands-on data fluency: you can navigate a database schema, write SQL to investigate a labeling anomaly, and form a diagnosis without waiting for a data engineer to pull the report for you.
Direct experience with or deep understanding of ML data pipelines and data labeling ecosystems, including annotation workflows, quality sampling methodologies, labeling taxonomy design, inter-annotator agreement, and the levers that drive or degrade training data accuracy.
Proven ability to identify systemic workflow problems across multiple concurrent pipelines, propose targeted improvements, and drive adoption across teams.
Approaches problems with intellectual curiosity and analytical rigor, taking the time to form a well-supported point of view and communicate it with confidence.
Exceptional communication: you translate a nuanced data quality finding into a precise business or safety risk that a senior executive can act on.
Bachelor's degree in a technical or business discipline, or equivalent practical experience.

Nice To Haves

Prior experience in autonomous vehicles, robotics, computer vision, or ML model training pipelines.
Background in ML engineering, data engineering, or technical consulting.
Experience managing large-scale offshore or globally distributed annotation teams.
Demonstrated track record of improving training data quality or labeling accuracy at scale, with metrics to show for it.

Responsibilities

Own the data quality standard across 10+ labeling pipelines. Establish what "good" looks like for each data type, instrument the pipelines to measure against it, and track gaps with rigor.
Identify and close quality gaps. Audit live workflows, query databases, trace accuracy failures to their structural root cause, and return with a specific, evidence-based improvement plan.
Operate above the execution layer. Partner with operations project managers who own throughput and delivery, and provide the quality diagnostic lens they are not positioned to hold.
Design and implement scalable processes to improve labeling accuracy, reduce systematic errors, and support evolving ML training requirements.
Serve as the strategic interface between Autonomy Engineering and ML Operations, connecting labeling quality metrics directly to model performance and safety outcomes.
Build executive-ready reporting that frames quality gaps and improvement progress as model performance and safety signals, not just operational metrics.
Drive cross-functional alignment across engineering, product, and global ops teams by bringing clear analysis and well-reasoned recommendations to every conversation