Senior ML Research Scientist, Pegasus

Who we are

At TwelveLabs, 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 $110+ 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.

Our partnership with NVIDIA and AWS gives us access to the most advanced chips, including B300s, enabling us to push the boundaries of what's possible in video AI.

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 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.

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