AI Data Operation Lead, Autonomy

Rivian•Palo Alto, PA

About The Position

Rivian Autonomy is seeking an AI Data Operation Lead within its AI Platform organization to lead the people and processes that turn vehicle data into reliable training and evaluation datasets. The role brings together labeling delivery, data quality, curation, and human review to support autonomous-driving model development. The lead manages internal teams and external partners and is accountable for delivery, quality, and cost. They ensure data is usable before annotation, accepted labels meet model-team requirements, and reviewers turn difficult driving events into actionable feedback for engineering. The role works closely with Perception, Planning, Data Infrastructure, Eval Infrastructure, and Product to translate model-development priorities into operational plans and provide evidence for release decisions. It requires developing managers and specialists, making prioritization tradeoffs when demand exceeds capacity, and building an operation that improves as models, tools, and the vehicle fleet evolve.

Requirements

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.
Sufficient technical depth in AI development to discuss data requirements and failure analysis with engineers and turn those discussions into clear operational work.
Track record of developing managers and specialists, establishing ownership, and adapting team structures as priorities change.
Strong analytical judgment to distinguish quality, capacity, and tooling problems, test their causes, and follow through on solutions.
Clear communication across engineering, product, finance, and operations, including the ability to make prioritization decisions and explain their delivery impact.

Nice To Haves

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.

Responsibilities

Set the operating plan for AI data operations, develop team leaders, define responsibilities across employees, contractors, and vendors, and build capacity plans covering annotation, quality review, and rework across concurrent programs.
Translate training and evaluation needs into labeling specifications, reviewer guidance, delivery milestones, and acceptance criteria; collaborate with model teams on taxonomy changes and align delivery with development cycles.
Own operational checks that determine whether collected vehicle data is ready for curation and labeling, and partner with collection and engineering teams to resolve sensor, calibration, and data-quality issues.
Set vendor service levels and quality expectations, evaluate suppliers, manage capacity, pricing, and budgets with Finance and Procurement, reconcile accepted work with invoices, and address delivery or quality shortfalls.
Build human-review workflows with autonomy and systems engineers to investigate driving events, classify recurring failures, and assemble evidence for root-cause analysis; feed findings into scenario selection, evaluation datasets, and targeted data requests.
Establish shared metrics for accepted-data volume, quality, turnaround time, backlog, rework, collection yield, and cost; use them to identify constraints, allocate review capacity, and make delivery commitments.
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.