Staff Program Manager, Human & LLM Data Annotation Operations
R
RedditData annotation
Completely remote friendly within the United StatesFull-TimeStaff
Salary$180,200 — $252,300 USD
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Job Details
- Experience
- 7+ years of experience in data operations, program management, or vendor operations with a heavy focus on data labeling, model training data, or taxonomy infrastructure.
- Required Skills
- SQLQuality Assurance
Requirements
- 7+ years of experience in data operations, program management, or vendor operations focused on data labeling, model training data, or taxonomy infrastructure.
- Ability to align Product and Engineering leadership, set boundaries, and enforce structured prioritization.
- Ability to translate vague product requirements into detailed labeling instructions, edge-case trees, and taxonomy classification schemas.
- Experience leveraging LLMs, synthetic data generation, or automated tooling to augment vendor annotation workflows.
- Experience partnering with Vendor Operations or Vendor Managers to turn project requirements into executable guidelines and handle task-level escalations.
- Experience across Ads or Monetization and Consumer-facing product organizations is preferred.
- Strong proficiency in SQL and data manipulation to analyze vendor quality metrics, pipeline throughput, and project completion SLAs is preferred.
Responsibilities
- Own and scale the incoming request process across Consumer and Ads, prioritizing initiatives against agreed criteria and business impact.
- Design and execute the operational roadmap for integrating LLM-assisted labeling workflows with human vendor operations.
- Standardize taxonomy definition guidelines and operationalize multi-quarter labeling projects for Consumer product experiments and Ads safety systems.
- Partner with Product Managers, Data Scientists, and Operations Leaders to align labeling capabilities with business objectives.
- Translate high-level product concepts into executable human and machine tasks.
- Partner with the Vendor Manager to hand off task guidelines, resolve complex edge cases, and establish feedback loops.
- Build and standardize request intake models, SLA frameworks, quality assurance practices, and throughput metrics.
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