The posting
Who We Are
Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides.
Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles.
With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected.
Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors.
About the Team
The Perception ML Operations team sits at the center of how Nuro's vehicles learn to see. We are responsible for the training and evaluation data that powers perception models across every sensor modality: vision, lidar, radar, and beyond. We work alongside ML engineers, product owners, data engineers, and external labeling partners to turn ambiguous, fast-moving requirements into data deliverables the Perception team can build on with confidence.
This is a team that operates at the intersection of ML urgency and operational discipline. Here is what the work actually looks like day-to-day:
- Sitting in a roadmap review, hearing a new feature requirement land, and walking out with a concrete data collection and labeling plan already scoped against timeline and budget.
- Reviewing vendor throughput against milestone dates and catching a capacity gap before it would become a release risk.
- Querying pipeline data directly to understand where quality is slipping, rather than waiting for a ticket or ML engineer to surface the problem.
- Being the person Perception Engineering calls when they need to know whether a data deliverable is real, on time, and within budget, and making sure the team you manage translates that bar into terms vendors can execute against without ambiguity.
If that sounds like the kind of ownership that energizes you, we would love to talk.
About the Role
This is a role built for someone who wants to own outcomes, not just track them. You will translate company-wide perception feature requirements into concrete data deliverables, manage vendor allocation and budget against Nuro's milestone and release timelines, and keep a clear, defensible view of outcomes, risk, and data quality across the pipelines you own — distinguishing a one-off labeling error from a systemic labeling process problem, and closing the gap either way.
The work connects directly to Nuro's mission. Perception models are only as good as the data behind them, and every gap in coverage, every misallocated vendor hour, and every quality blind spot eventually shows up in how the vehicle sees the world. You are the person who catches that upstream, before it becomes an engineering fire drill or a safety question.
You'll partner closely with ML engineering leadership, product owners, and vendor operations, with the access and visibility to make real calls on scope, budget, and quality tradeoffs. What we ask in return is rigor, clear communication, and genuine ownership of the outcome.
About the Work
Translate Requirements into Deliverables
- Synthesize company-wide perception feature needs into detailed, actionable data deliverables covering vision, LiDAR, and additional sensor modalities.
- Own the end-to-end labeling process, from data availability through an outcome Perception can train on, directing data annotation partners to execute against that bar.
Own Vendor Allocation and Budget
- Manage vendor allocation and budgeting in direct alignment with Nuro's milestone and release timelines.
- Oversee large-scale vendor and managed service provider relationships against quality, latency, and volume targets, and hold them accountable when performance drifts.
Track Outcomes, Risk, and Quality
- Define and maintain clear, current tracking of outcomes, risk, and data quality so that status is never a surprise to engineering or leadership.
- Own quality as an outcome, not just a status: define what "good" looks like for deliverables within your pipelines, and hold vendors to that bar before problems surface downstream.
- In response to quality dips, identify whether the issue is an isolated labeling error or a broader process failure, and address the root cause rather than just the single error.
- Translate high-level product roadmaps and engineering requirements into granular operational execution plans, with resource allocation and throughput modeled explicitly.
- Query databases directly using SQL (Python a plus) to analyze throughput and quality metrics and generate reporting without waiting on engineering support.
About You
We are not looking for someone who has done everything. We are looking for someone who thinks in systems, owns outcomes rather than status updates, and has genuine command of both the operational and technical sides of perception data.
What You Bring
- 5+ years of experience in Technical Program Management or Operations Strategy, managing complex, cross-functional programs within technology, autonomous driving, or AI/ML.
- Demonstrated ability to translate high-level product roadmaps and engineering requirements into granular operational execution plans, including managing resource allocation and throughput for large-scale workflows.
- Experience overseeing large-scale vendor operations or managed service providers, specifically against quality, latency, and volume targets.
- Proficiency with SQL to query databases, analyze throughput metrics, and generate reports independently.
- A track record of holding vendors accountable to a quality bar you've defined yourself, not just monitoring metrics someone else set.
Bonus Points
- Background in autonomous vehicles, robotics, computer vision, or ML model training.
- Prior experience in ML engineering, data engineering, or technical consulting.
- Demonstrated track record of improving data quality or throughput at scale, with metrics to show for it.
- Familiarity with autonomous vehicle sensor modalities (LiDAR, Radar, Camera, Thermal) and their respective data annotation requirements.
- Bachelor's degree in a technical or business discipline, or equivalent practical experience.
At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $132,000 and $198,000 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.
At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP
