ML Data & Annotation Operations

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