ML Data & Annotation Operations
This role leads the end-to-end data operations lifecycle for machine learning systems, translating research and product requirements into scaled annotation workflows and quality standards. Professionals in this position design data collection strategies, manage vendor partnerships and internal labeling teams, and establish comprehensive quality frameworks including guidelines, metrics, and escalation processes. Unlike individual contributors focused solely on annotation tasks, these operators own strategic decisions around tooling, process optimization, and workforce development to ensure datasets meet rigorous quality standards at scale. They typically report to heads of data or research operations and collaborate directly with ML engineers, researchers, and product teams to align data needs with model training priorities.
Measured across 18 of 19 open postings.
“Building labeling pipelines using LLM-as-a-judge”
“Develop lightweight Python/SQL pipelines and tooling to make data work faster and reproducible”
“familiar with how LLMs work or have strong interest in understanding AI training methodologies”
“Comfort working in fast-moving, ambiguous environments; Experience in AI, data infrastructure, marketplaces, platforms, or high-growth technology companies”
Skills
What companies are looking for in this role.
Data quality and governance
Annotation schema design
Program and project management
Process design and improvement
Training data curation
Supplier and vendor management
Business insight analysis
ML systems development
Deal structuring and negotiation
Quality management systems
SQL query development
Dashboarding and self-service analytics
Incident response and reliability
Operational metrics and analytics
Data pipeline engineering
Data annotation operations
AI evaluation design
Change management
Technology
The tools and technologies that define this role.
Open Jobs
19 open ML Data & Annotation Operations jobs across 15 companies.
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