Machine Learning Data Annotator ›

Filevine is a Legal AI company delivering Legal Operating Intelligence for the future of legal work. Grounded in a singular system of truth, Filevine brings together data, documents, workflows, and teams into one unified platform—where modern legal work happens with clarity and consistency. Powered by LOIS, the Legal Operating Intelligence System, Filevine connects context across every matter to transform legal operations from reactive to proactive. LOIS reads, understands, and reasons across your data to surface insight, automate complexity, and give professionals the clarity and confidence to see more, know more, and do more. Fueled by a team of exceptional collaborators and innovators, Filevine’s rapid growth has earned AI awards and recognition from Deloitte and Inc. as one of the most innovative and fastest-growing technology companies in the country. Role Summary: We are seeking a Machine Learning Data Annotator to support the development of AI-driven products by producing high-quality annotations for machine learning tasks. These annotated datasets will be used across the ML pipeline, including model training, evaluation, retrieval, and quality analysis. The role also includes performing error analysis of model outputs, comparing the performance of competing approaches, collaborating with cross-functional teams to improve datasets and workflows, and documenting issues and recommendations to support continuous improvement. This role is essential for ensuring data accuracy, consistency, and completeness, directly impacting the performance of our AI-driven products. This is a full-time independent contractor position. Responsibilities Annotate and label data in accordance with project-specific annotation definitions and guidelines Ensure annotation accuracy, consistency, and adherence to the defined scope Apply an appropriate level of detail when labeling structured data, text spans, or bounding boxes Identify and flag ambiguities, edge cases, and data quality issues Flag cases that require clarification or further context Collaborate with machine learning engineers to optimize annotation specifications and workflows Adapt to guideline updates and iterative task refinements Maintain confidentiality of sensitive and proprietary data Qualifications Strong interest in machine learning, AI, and data-driven work Strong attention to detail and ability to follow guidelines precisely Ability to make consistent judgments when working with ambiguous data Comfortable communicating and collaborating in a team environment Ability to work independently and manage time efficiently Proficiency in English, both written and spoken Prior experience with data annotation or related technical tasks is highly desirable