Senior Program Manager, Data Quality

Who We Are

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world's most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future.

About the Team

Every vehicle that drives itself on a public road starts with a decision someone made about a single annotation. At Nuro, we take that seriously - and we're looking for someone who does too.

ML Operations is where data becomes decisions. We sit at the intersection of Autonomy Engineering and the globally distributed annotation teams who label the world our vehicles need to understand. We are builders, diagnosticians, and systems thinkers who care deeply about the end product - because we know that what we fix here shows up on the road.

This team is collaborative and technically rigorous. We work closely with engineering and we hold each other to a high standard, but we also invest in getting things right together. Here is what the work actually looks like day-to-day:

* Investigate annotation faults by querying databases directly to identify root causes, moving beyond reliance on ticket queues for initial troubleshooting.

* Sitting with Autonomy Engineering leadership, translating a model accuracy regression into a specific labeling workflow problem and walking in with the fix already scoped.

* Auditing a live pipeline, spotting a systematic edge-case misclassification that has been silently propagating, and building the evidence-based case to restructure how that object type gets labeled.

* Manage the processes for quality management/processes for the offshore annotation team

* Being the bridge: the person ML Operations leans on for quality diagnostics, and the person Engineering trusts to understand how a labeling decision upstream shapes what the model learns downstream.

If that sounds like the kind of problem-solving that lights you up, we would love to talk.

About the Role

This is a rare opportunity to own something that genuinely matters. You will co-own a portfolio of active labeling pipelines alongside senior leadership with real authority to set the quality standard, measure against it, and close the gaps. Not to report on pipelines. To make them better.

The work connects directly to Nuro's mission. A single systematic flaw in annotation can propagate silently through training and surface as a safety regression on the road. You are the person who finds it before that happens and who builds the systems that prevent it from happening again. That kind of upstream impact is hard to find in most roles. Here, it's the whole job.

You'll be well-supported: partnering closely with senior engineering and operations leadership, with the access and visibility to do your best work. What we ask in return is curiosity, rigor, and genuine care about getting it right.

About the Work

You will own the quality diagnostic layer across our labeling pipelines: defining the standard, building the instrumentation, and closing the gaps that matter most to model safety and performance.

Own the Quality Standard

* Define what 'good' looks like for each data type across active labeling pipelines and instrument pipelines to measure against it continuously, not just at delivery milestones.

* Build inter-annotator agreement frameworks, taxonomy governance, and sampling methodologies that hold up at offshore production scale.

* Design scalable processes to reduce systematic errors and support evolving ML training requirements.

Diagnose and Close Gaps

* Audit live workflows, query production databases, and trace accuracy failures to their structural root cause then return with an evidence-based plan that fixes the mechanism, not just the symptom.

* Apply statistical process control thinking to distinguish a labeling error from a labeling…