Staff AI Application Engineer, Product

WHO WE ARE: At Twelve Labs, we are pioneering the development of cutting-edge multimodal foundation models that have the ability to comprehend videos just like humans do. Our models have redefined the standards in video-language modeling, empowering us with more intuitive and far-reaching capabilities, and fundamentally transforming the way we interact with and analyze various forms of media. With a remarkable $107 million in Seed and Series A funding, our company is backed by top-tier venture capital firms such as NVIDIA’s NVentures, NEA, Radical Ventures, and Index Ventures, and prominent 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. We are a global company that values the uniqueness of each person’s journey. It is the differences in our cultural, educational, and life experiences that allow us to constantly challenge the status quo. We are looking for individuals who are motivated by our mission and eager to make an impact as we push the bounds of technology to transform the world. Join us as we revolutionize video understanding and multimodal AI. ABOUT THE ROLE: TwelveLabs builds multimodal foundation models and products for multimodal intelligence and orchestration. The quality of those models and products depends on the quality of the data and evaluation work behind them, and that work depends on the internal tools the team uses daily. This role will own and build internal products to support model evaluation, regression testing, dataset discovery, and agentic context curation. This is a hybrid, high autonomy role and the first on this team. You will have full ownership and drive implementation of mission-critical internal products for all members of TwelveLabs. YOU WILL: Unify the internal data and evaluation platform. Pull the internal beta access, evaluation, regression, and dataset discovery/cataloging products into one maintainable system that science, product, and engineering use every day. Build a secure access layer over very large data. Make our internal data discoverable and queryable across cloud environments, at petabyte scale, with the access controls and PII handling needed to work safely with production and customer content. Bring real customer signal into the loop. Integrate feedback and usage data from our self-serve product so the team can ground datasets and evaluations in actual customer behavior rather than abstractions. Support the product org's internal agentic content curation. Help build and harden the internal agent the product team uses to pull context across its tools and data, and move it from prototype toward something the wider org can use, with proper access controls behind it. YOU MAY BE A GOOD FIT IF YOU HAVE: - Strong backend engineer with real depth in at least one language (Python, Go, or similar). You design data models, services, and APIs that hold up under real use. - Genuine system design instinct. You can take an ambiguous problem, decide what to build, and make architectural calls you can defend. - A track record of building zero to one internal products and shipping them. - Comfortable as the primary or sole engineer building the foundations for a larger team that can easily onboard onto what you’ve built. - Comfort owning a product outright: scoping it, building it, shipping it, and supporting internal users, while keeping sight of how it fits into science, product, and go to market. - Experience with large unstructured datasets. - Full stack capable. You can stand up a usable interface when the work calls for it, but your center of gravity is the backend. We pair engineers with tools like Claude Code for much of the frontend lift, so deep frontend specialization is not what this role needs. YOU’LL STAND OUT IF YOU HAVE: - Data platform, lakehouse, or pipeline experience across ob