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# AI Data Operation Lead, Autonomy

- **Company:** Rivian
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, Data Infrastructure, Machine Learning, DataOps, Large Language Models
- **Published:** October 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=21a295af6d458e56

## About the Role

* 10+ years leading operations, including at least 5 years supporting autonomous vehicles, robotics, or complex machine-learning programs, with responsibility for budgets or a P&L.
 * Experience managing annotation partners across domestic and international locations, including commercial negotiations, staffing plans, and service delivery.
 * Enough technical depth in AI development to discuss data requirements and failure analysis with engineers and turn those discussions into clear work for operations teams.
 * A record of developing managers and specialists, establishing ownership, and adapting team structures as priorities change.
 * Strong analytical judgment: you can separate a quality problem from a capacity or tooling problem, test the cause, and follow through on the fix.
 * Clear communication across engineering, product, finance, and operations, with the ability to make prioritization decisions and explain their impact on delivery.

Bonus Points

 * Direct experience supporting autonomous driving or other physical AI systems through data production and evaluation.
 * Experience applying large language models, multimodal models, or generative AI to annotation and human-review workflows.

## Description

You will lead internal teams and external partners, with accountability for delivery, quality, and cost. This includes making sure the data we send for annotation is usable, that accepted labels meet the model teams' requirements, and that reviewers can turn difficult driving events into actionable feedback for engineering.
You will work closely with Perception, Planning, Data Infrastructure, Eval Infrastructure, and Product to translate model-development priorities into operational plans and provide evidence for release decisions. We are looking for a leader who can develop managers and specialists, make clear tradeoffs when demand exceeds capacity, and build an operation that improves as our models, tools, and vehicle fleet evolve.
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Lead the organization. Set the operating plan for AI data operations, develop team leaders, and define responsibilities across employees, contractors, and vendors. Build capacity plans that account for annotation, quality review, and rework across concurrent programs.

 * Deliver data that model teams can use. Translate training and evaluation needs into labeling specifications, reviewer guidance, delivery milestones, and acceptance criteria. Work with model teams on taxonomy changes and keep delivery aligned with their development cycles.
 * Improve data quality and yield. Own the operational checks that determine whether collected vehicle data is ready for curation and labeling. Partner with collection and engineering teams to resolve sensor, calibration, and data-quality issues before they consume downstream annotation capacity.
 * Own vendor performance and spend. Set service levels and quality expectations, evaluate suppliers, and manage capacity, pricing, and budgets with Finance and Procurement. Reconcile accepted work with invoices and address delivery or quality shortfalls through clear corrective actions.
 * Develop human evaluation and triage operations. Build review workflows with autonomy and systems engineers to investigate driving events, classify recurring failures, and assemble the evidence needed for root-cause analysis. Feed those findings into scenario selection, evaluation datasets, and targeted data requests.
 * Run the operation with consistent metrics. Establish a shared view of accepted-data volume, quality, turnaround time, backlog, rework, collection yield, and cost. Use these measures to identify constraints, allocate review capacity, and make delivery commitments teams can plan around.
 * Improve tools and automation. Partner with Data Infrastructure, Eval Infrastructure, and Applied General AI on labeling tools, prelabels, automated quality checks, and assisted triage. Define operational requirements and validate improvements against measured quality and reviewer effort before scaling them.