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 PEGASUS Pegasus is TwelveLabs' core video understanding product, turning video into useful analysis by reasoning over visuals, speech, audio, and on-screen text. The team is not building a generic Video LLM in isolation; we build customer-facing video intelligence workflows that require temporal understanding, structured outputs, and production-grade reliability. A key example is Segment, our time-based metadata capability. Instead of asking the model a broad question about a video, customers define the exact segment types they care about and the metadata fields they want back. Pegasus then finds the relevant start and end times and returns structured metadata for each segment, such as titles, summaries, topics, people, visual subjects, confidence, or domain-specific labels. This is designed for workflows where “what happened” is not enough; customers need to know when it happened and receive metadata that can flow directly into search, archive, editing, compliance, or content management systems. For example, a news archive customer can define a segment type like editorial_narratives and ask Pegasus to split a long broadcast into individual stories. For each story, Pegasus can return a timestamped segment with fields such as segment_title, description, editorial_subjects, visual_subjects, names, and confidence. The output is not just a summary of the full video; it is a structured timeline of the video, aligned to the customer's schema. This is the distinction that matters for Pegasus: general video analysis answers questions about video, while Segment turns video into time-based, structured data tailored to a specific business workflow. Learn more about Pegasus! - Building Structured Video Assets: A Time-Based Metadata (TBM) Pipeline https://www.twelvelabs.io/ko/blog/%EB%B9%84%EB%94%94%EC%98%A4%EB%A5%BC-%EA%B5%AC%EC%A1%B0%ED%99%94%EB%90%9C-%EC%9E%90%EC%82%B0%EC%9C%BC%EB%A1%9C-time-based-metadata(tbm)-%ED%8C%8C%EC%9D%B4%ED%94%84%EB%9D%BC%EC%9D%B8-%EA%B5%AC%EC%B6%95%EA%B8%B0 - Quick Shorts demo: YouTube Shorts https://www.youtube.com/shorts/uaEcQ07AKGg ABOUT THE TEAM The Pegasus team sits at the core of TwelveLabs' video understanding capabilities and is responsible for driving Pegasus, our Video Analysis product. Our focus is on developing multimodal video analysis systems that are designed for high instruction following capability and producing highly complex, hierarchically structured outputs. We focus on shipping products with real-world value rather than doing research in isolation, and we work in a goal-oriented, cross-functional team that encompasses both ML researchers and engineers. Our work covers a broad range of challenges: large-scale distributed training of multi-modal LLMs that span from pre-training to RL, accurate temporal segmentation and structured metadata extraction for real-world use cases, extending temporal context length to multiple hours, and data curation processes that enable well-aligned evaluation and performance improvements through training data enhancements. Our team has access to the most advanced chips in the world, including NVIDIA B300s, to push the boundaries of video analysis systems—accelerating our research-to-production cycle as fast as possible. IN THIS ROLE, YOU WILL - Drive research on Pegasus's harder problems such as temporal segmentation, multi-hour context, structured output generation, and training strategies from pre-training through RL, where the right approach requires deep judgment. - Design rigorous experiments and evaluation methods that produce clear signals on complex multimodal problems, including where ground truth is ambiguous. - Strengthen the team's research approach by helping reframe problems, sharpen hypotheses, and raise the bar for experimental rigor. - Work closely with ML Engineers to translate research advances into production, informing tradeoffs around architecture, serving, and system design. - Communicate research findings clearly and use them to inform technical direction across the team. - Explore and adopt AI-assisted development tools such as Claude, Gemini, and GPT to improve productivity across coding, experimentation, debugging, and documentation. 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. YOU MAY BE A GOOD FIT IF YOU HAVE - Significant research experience in one or more areas relevant to video understanding, such as multimodal LLMs, large-scale distributed training, temporal modeling, data-centric model development, computer vision, or vision-language systems, with demonstrated depth in at least one. - A track record of driving research on problems with significant technical ambiguity, demonstrated through projects, publications, or tech