Project Management Lead - AI Data Service Operations (Linguistic Intelligence)

About the Team
The AI Data Service and Operations (ADSO) team is responsible for providing safety and non-safety data annotation services and search operation services for all of the company's international products, which can also help international products build their own data ecological security.

The Linguistic Intelligence team specializes in large-scale data delivery to enable both foundation model and product-oriented model development, powering our products and AI research. We deliver high-quality, high-volume datasets that support multilingual capabilities and the full lifecycle of multimodal model training—from data generation and annotation to continuous iteration and improvement.

What will I do?
As the Project Management lead, you will own end-to-end delivery of large-scale AI data programs, with accountability for quality, progress, cost, and risk across multiple concurrent projects. You will directly lead experienced AI Project Managers and operate through cross-functional, matrix teams (annotation/delivery teams do not report to you) to ensure predictable, high-quality data delivery. You will also drive operational excellence through process standardization, data-driven governance, and AI-enabled productivity.

Responsibilities
1. Program & Delivery Leadership
- Lead a portfolio of AI data projects end-to-end, ensuring delivery on time, on quality, and within budget.
- Establish delivery governance: milestones, capacity planning, dependency management, change control, and risk mitigation.

2. Quality & Process Excellence
- Define and enforce quality standards (sampling/audits, acceptance criteria, defect taxonomy) and drive RCA/CAPA to reduce rework.
- Partner with stakeholders to improve guidelines and SOPs; continuously optimize workflows and operational metrics.

3. Stakeholder & Cross-Functional Management
- Align product, algorithm/model, platform, policy/compliance, and operations teams on scope, success criteria, trade-offs, and launch readiness.
- Provide clear, data-driven reporting and executive-ready updates; resolve escalations and priority conflicts.

4. Vendor & Cost Management (as applicable)
- Manage vendor performance via SLAs/KPIs, capacity planning, and quality governance; drive continuous improvement and cost efficiency.
- Own or influence budget planning/tracking and unit economics (cost per unit, throughput, cycle time).

5. People Leadership
- Hire, coach, and performance-manage experienced AI project managers; set delivery standards, templates, and operating cadence.
- Ensure consistent project management practices and a strong delivery culture in a high-tempo environment.

6. AI-Enabled Productivity: Apply AI tooling to improve delivery efficiency and quality (e.g., documentation, guideline iteration, quality support, analytics automation) in compliance with internal policies.

Minimum Qualifications
- Bachelor’s degree or above.
- At least 3 years of regional team management experience along with proven experience in leading an experienced project management team.
- At least 5 years of program/project management experience, along with proven end-to-end delivery experience in at least one of: LLM data programs, AIGC, data annotation/labeling, evaluation datasets, or related AI data operations.
- Demonstrated strength in quality management, schedule management, risk/change control, and data-driven decision-making.
- Strong cross-functional stakeholder management experience across regional/global teams.
- Excellent proficiency in both written and spoken English
- Strong ability to perform in a fast-paced environment with frequent context switching; resilience in roles that may include exposure to harmful content.

Preferred Qualifications
- Familiarity with project management methodologies and tools (e.g., Agile/Waterfall/Hybrid; Jira/Asana/Smartsheet/Confluence, etc.).
- Strong vendor management experience (SOW/SLA/KPI management, performance governance).
- Hands-on experience improving operations with automation and AI tools.
- Proven experience designing and deploying an AI agent (e.g., for workflow automation, QA support, triage, reporting, or knowledge management) with measurable impact.