ML Data Project Manager, Twelve Labs

• 2+ years of experience working in a data operations organization
• Experience with gathering, labeling, and analyzing data, including familiarity with popular annotation tools and workflows
• The ability to analyze messy and complex data, identify overarching patterns, and distill your findings into crisp annotation guidelines or other accessible documentation
• Excellent communication and project management skills, and the ability to work with both internal and external teams
• The ability to support several projects simultaneously and to accept reprioritization as needed
• A high-level understanding of the workings of LLMs, VLMs, and/or prompt engineering
• Conviction that data is the key ingredient for the performance of AI models

Desirable

• You will be a vital member of our ML Data Team – the team which delivers the data and “ground truth” labels that are critical to our efforts to build world class video models (currently State of the Art [SoTA] on several industry benchmarks!)
• Your role will primarily be delivery focused and include responsibilities such as defining dataset needs and requirements in consultation with our research and product teams; designing and building data pipelines; and coordinating with our vendor partners that execute at scale
• You will also be responsible for automating as much of the repetitive partnership and annotation-quality-evaluation work as possible
• A desire to work cross functionally and to build relationships is critical for success in this position
• Plan, design, and execute data collection and labeling projects from start to finish
• Build and keep up solid relationships with our outside vendors and contractors
• Ensure our collaboration is smooth and valuable
• Create labeling instructions and evaluate data quality
• Make sure we've got a good mix of quality, diversity, and quantity of data
• Brainstorm ways to make our tools or instructions more user-friendly
• Keep tabs on ongoing projects to make sure we're putting our resources in the right places
• Be ready to tweak project scope and instructions when new information comes in
• Share updates on projects, including by building diagnostics/dashboards and data analysis tools/reports
• Keep an eye out for automation opportunities in any of the above to make things easier over time