Staff AI Application Engineer, Twelve Labs

• 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
• Even if there are a few checkboxes that aren’t ticked through your prior experience, we still encourage you to apply! If you are a 0-1 achiever, a ferocious learner, and a kind and fun team player who motivates others, you will find a home at TwelveLabs

Desirable

• 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
• 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