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 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!
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 technical contributions.
Strong proficiency in Python and PyTorch.
Exceptional experimental judgment, including the ability to design evaluations for complex multimodal problems, run rigorous ablations, and draw clear conclusions from empirical results.
Strong communication skills and a track record of strengthening others' research through collaboration — helping formulate sharper hypotheses, identify more informative experiments, or reframe problems more tractably.
Preferred qualifications
Experience working on multimodal systems involving video, vision, language, or structured output generation.
Experience improving model quality through data curation, evaluation design, or training data enhancements.
Experience with large-scale distributed training in high-performance GPU environments.
Experience translating research advances into production ML systems.
Experience defining research direction within a team or project.
MS, PhD, or equivalent practical experience in Machine Learning, Computer Science, or a related technical field.
Others
Hiring Process
Application Review → Recruiter Interview (비대면/30분) → Loop Interview [Hiring Manager Interview&Live Coding Test Interview] (대면/약 90분) → System Design Interview(대면/약 90분) → Final Round Interview (비대면/약 30분) → Reference Check → Offer
Benefits and Perks
Growth & Tools
글로벌 B2B 고객과 함께 성장하는 Global Team
자율성과 협업을 모두 갖춘 하이브리드 근무
최신 맥북 및 70만 원 상당 재택근무 장비 지원, 3년 주기로 최신 장비 교체
Tokens never sleep - Tech 직군 LLM 토큰 무제한 지원
강의, 컨퍼런스, 멤버십 등에 사용 가능한 연 140만원 상당 자기개발비 지원
영어 교육 프로그램 및 글로벌 버디 프로그램 운영
야간 및 주말 출퇴근 택시비 지원
Meal & Snack
식비·교통비 등 자유롭게 사용할 수 있는 연 720만원 상당 법인카드 제공
사무실 내 스낵바 운영 (간식, 커피, 제철 과일 등)
사무실 근무 시, 오후 7시 이후 저녁 식대 제공
Wellness & Family