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.
Skills
What companies are looking for in this role.
Designing and executing large-scale data collection projects from specification through delivery
Creating and documenting clear annotation guidelines and labeling standards for complex datasets
Monitoring and assuring data quality through systematic review and metrics
Managing vendor partnerships and external data annotation teams
Managing project timelines, resource allocation, and deliverables across multiple concurrent initiatives
Building and optimizing data pipelines for collection, labeling, and post-processing workflows
Automating repetitive operational processes to improve efficiency and reduce manual work
Analyzing patterns in complex and unstructured data to derive actionable insights
Translating machine learning model requirements into concrete data specifications
Designing inter-annotator agreement frameworks and quality measurement methodologies
Conducting hands-on data annotation and labeling tasks to maintain subject matter expertise
Building evaluation pipelines to measure model quality and detect performance regressions
Conducting robustness testing and identifying edge cases and failure modes in model outputs
Understanding multimodal and vision-language model architectures and their data requirements
Designing context and prompt engineering strategies to guide model behavior
Communicating technical findings and project status to cross-functional research and product teams
Collaborating effectively across engineering, research, and product teams with different priorities
Training and calibrating annotation teams to maintain consistent quality standards
Adapting workflows and processes based on real-time feedback and changing project needs
Identifying and documenting patterns in disagreements between annotators
Providing constructive feedback and fostering continuous improvement in data operations
Gathering and incorporating feedback from annotators on tool usability and process improvements
Technology
The tools and technologies that define this role.
Open Jobs
15 open ML Data & Annotation Operations jobs across 8 companies.
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