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Uber AI Solutions is one of Uber’s biggest bets with the ambition to build one of the world’s largest data foundries for AI applications and evolve into a platform of choice for a variety of online tasks. AI Data Labelling operations is one of the core functional teams within Uber AI Solutions with the responsibility to oversee the end-to-end lifecycle of the data annotation programs for B2B clients.
Job Responsibility:
Define the programs and its key objectives to support LLM model training
Drive cross-functional efforts across Operations, Product, Eng and Legal to define the Program-level Ops strategy
Define scalable data labeling workflows leveraging internal tools, external vendors, and automation
Shape task/ product/ feature launches and improvements
Lead cross-functional initiatives to build, scale, and optimize data annotation programs
Own program delivery across internal teams, vendor partners, and ML stakeholders
Define roadmaps, manage SLAs, create scalable processes, and resolve bottlenecks
Operational excellence - Define and drive end-to-end execution of large-scale annotation programs across multiple data types
Collaborate with ML, product, and data operations teams to scope and prioritize labeling needs
Own vendor engagement: onboarding, SLA management, training, and quality reviews
Build feedback loops between annotators and model performance to inform labeling strategies
Create dashboards and reporting mechanisms to track labeling velocity, quality, and cost
Lead initiatives to improve labeling efficiency through tooling enhancements and process automation
Be the voice of labeling in cross-functional forums—translating model needs into operational plans
Customer engagement - Drive daily, weekly and monthly meetings, business reviews and reports
Requirements:
7+ years of program management experience, ideally in ML ops, data labeling, or human data operations
Proven track record managing multi-vendor operations or global labeling teams
Strong understanding of AI/ML lifecycle stages and the importance of annotated data quality
Experience defining SOPs, rubrics, audit mechanisms, and workflows for scalable data labeling
Proficient in project management tools
Strong analytical and communication skills
ability to synthesize feedback from ML, ops, and product stakeholders
Nice to have:
Exposure to LLMs, foundation model training and human in the loop (HITL) operations
Familiarity with annotation for multimodal inputs (e.g., Audio, Video, Image, Text, Documents, OCR based forms etc)
Experience of working in customer facing roles
Experience managing budgets, metrics, and KPIs across distributed teams
Knowledge of quality scoring frameworks, defining quality rubrics and QA loop design
Technical background (e.g., in ML, data science, or engineering) is a plus
What we offer:
Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp