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.
Managing end-to-end data lifecycle from requirement definition through delivery and quality assurance
Designing and implementing data annotation guidelines and labeling standards
Establishing and monitoring data quality metrics and acceptance criteria
Translating machine learning research needs into concrete data specifications and workflows
Managing external vendor relationships and outsourcing partnerships for data collection and annotation
Implementing quality control processes and audit workflows
Building and leading data annotation and quality assurance teams
Designing evaluation frameworks and benchmarks for machine learning models
Designing and building data processing pipelines and infrastructure
Capacity planning and resource allocation for data operations at scale
Identifying and resolving edge cases and ambiguous annotation scenarios
Analyzing disagreement patterns and inter-annotator agreement to improve data quality
Optimizing annotation workflows and tool usability for efficiency and throughput
Conducting performance analysis on annotation teams and individuals
Collecting and processing multimodal data including images, video, audio, and sensor data
Implementing inter-annotator agreement frameworks and statistical quality measures
Performing statistical analysis and experimental design for data quality assessment
Designing model evaluation strategies and automated evaluation pipelines
Conducting human-in-the-loop model evaluation and feedback collection
Designing context and prompt engineering strategies for language model behavior
Cross-functional collaboration between data operations, machine learning, research, and product teams
Communicating complex technical requirements and progress across stakeholder groups
Attention to detail and consistency in applying complex logic across large datasets
Training and onboarding annotation teams and contractors
Problem-solving and troubleshooting complex operational challenges
Adaptability and learning new tools and processes quickly
Managing competing priorities and flexible direction adjustment based on real-time feedback
Performance management and team coaching
Technology
The tools and technologies that define this role.
Open Jobs
29 open ML Data & Annotation Operations jobs across 16 companies.
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