ML Data Project Manager

Who We Are: At TwelveLabs, we are pioneering the development of frontier multimodal foundation models that can see, hear and understand the world as humans do. Our models have redefined the standards in video-language modeling, allowing developers to build programs with state‑of‑the‑art semantic search, summarization and analysis capabilities. TwelveLabs has raised $107 million in Seed + Series A funding from world‑class VC & corporate partners: NVIDIA, NEA, Radical Ventures, Index Ventures, Snowflake and Databricks. Our advisory team features AI visionaries and founders such as Fei‑Fei Li, Silvio Savarese, Alexandr Wang and more. Headquartered in San Francisco, with an influential APAC presence in Seoul, our global footprint underscores our commitment to driving worldwide innovation. About the Role: 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. You will: 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. You may be a good fit if you have: 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. You’ll stand out if you have: 2+ years of experience with Python or other popular industry tools for automation. Experience working with third‑party SDKs. Experience in data collection and labeling for multimodal language models. Experience working with research scientists and engineers. Expertise or interest in video‑centric domains, such as sports, advertising, and content creation. Benefits and Perks: An open and inclusive culture and work environment. Work closely with a collaborative, mission‑driven team on cutting‑edge AI technology. Full health, dental, and vision benefits. Flexible PTO and parental leave policy. Office closed the week of Christmas and New Years. VISA support (such as H1B and OPT transfer for US employees). Compensation $94K – $115K • Offers Equity The base salary & equity offered for this position will depend on several factors, including location, experience, qualifications and business needs which are assessed during the interview process. #J-18808-Ljbffr