TwelveLabs is a video intelligence company that builds perception, knowledge, and reasoning systems to enable machines to perceive, understand, and reason about video.
WHO WE ARE Video is 90% of the world's data. Most of it is invisible to machines. TwelveLabs builds the intelligence layer to change that. Our multimodal AI models understand video the way humans do — across sight, sound, and motion — and power production-scale AI workloads across media, entertainment, sports, security, and government. We have raised more than $210 million from NEA, Radical Ventures, Amazon, NVIDIA, Snowflake, Databricks, Index Ventures, NAVER Ventures, Korea Investment Partners, Quadrille Capital, Red Bull Ventures, and AI pioneers including Fei-Fei Li, Silvio Savarese, and Alexandr Wang. We are a global company, headquartered in San Francisco with offices in Seoul, New York, and London, and employees around the world. We believe the differences in our cultural, educational, and life experiences make our products stronger. Building technology that understands the world in all its complexity requires people who see it from every angle. We are looking for individuals who are driven by hard problems and want their work to matter. Come build it with us! ABOUT THE JOCKEY Jockey is TwelveLabs' unified agentic system that reasons across your videos and images. It combines a reasoning model with a memory layer that builds a knowledge store from your corpus. No context window holds a video archive. We work at a million hours of video. A single model forward pass can tell you about one file; it can't reason across a corpus, and no context window closes that gap. Jockey decomposes a query, retrieves, segments, and reasons across thousands of videos and images. Point it at an archive, ask for a highlight reel or the best viral moments, and it returns timestamped cuts you can use. Corpus-level understanding you can act on is the whole product. Built for agents, not just people. As AI agents increasingly become the primary consumers of video, we're building production-grade infrastructure that scales to millions of hours while delivering reliable, high-quality results for both human users and autonomous agents. We build on models we own. Marengo, our embedding model, resolves a query like "the moment we almost missed the flight" into real retrieval. Pegasus, our video-language model, returns structured, timestamped moments on a schema you define. We ship and improve both continuously, so Jockey's quality compounds with every release — no re-integration for customers. Few teams get to build an agent on a stack they control end to end. Deep expertise, one system, open culture. Foundation models, knowledge construction, search, and the agent harness all live in one org. Each team owns its domain and is expected to have deep expertise in it — but like a Formula 1 team, we optimize for the global system, not local parts. A model gain that doesn't expand what the agent can do isn't a gain. We trace a single algorithm change through to end-system behavior, and share work in progress weekly, not just finished results. Anyone can pull the context they need from any team. ABOUT THE ROLE This position spans two tracks—Research Scientist and Research Engineer—determined by your strengths and interests. These roles exist on a spectrum rather than as discrete categories; both contribute to research and implementation. As a Research Scientist, you will explore fundamental questions in video reasoning, retrieval-guided QA, and multimodal understanding, driving research from hypothesis formulation to experiment design and analysis. You’ll investigate how video agents can interpret long-form content, connect evidence across modalities, and deliver reliable, structured outputs that reflect real user needs. As a Research Engineer, you will focus on translating research ideas into robust, scalable systems. You’ll build and optimize pipelines that support indexing, retrieval, and agentic workflows, and develop the infrastructure that accelerates experimentation and ensures our models perform reliably in production. Your work will bridge the gap between exploratory research and the systems that power real-world applications. Both tracks contribute to advancing our video agent capabilities, and both require a balance of curiosity, technical depth, and collaborative problem solving. YOU MIGHT BE A GREAT FIT IF YOU HAVE We’re looking for candidates with research or engineering experience in areas related to video understanding, multimodal retrieval, temporal reasoning, or agent-driven workflows that combine models, tools, and structured search. You should be able to define meaningful research questions, design experiments, and drive projects from ideation to execution while grounding your work in real user scenarios and practical constraints. Strong proficiency in Python and PyTorch is essential, along with the ability to clearly communicate technical concepts and collaborate across science, engineering, and product teams. Experience in building AI/ML agents and bringing them into production environments is highly preferred. This includes designing agent architectures, integrating them with retrieval or reasoning systems, and deploying them as reliable, user-facing components. We evaluate based on relevant technical skills and research experience rather than degrees alone, though this is typically supported by an MS/PhD or equivalent practical experience in a relevant field. *Even if you don't check every box, we encourage you to apply. If you're a zero-to-one achiever, a ferocious learner, and a kind team player who motivates others, you'll find a home at TwelveLabs. WHAT MAKES THIS ROLE UNIQUE The team sits at the intersection of models, retrieval, reasoning, and user workflows. The systems you build will rapidly make their way into real products used by customers worldwide. We operate with a tight research-to-production loop, ensuring that innovations have immediate, meaningful impact. OTHERS - Work Location: Seoul Itaewon office + Pangyo satellite office - Additi