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서울특별시, 대한민국
Hybrid
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!
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.
TwelveLabs builds multimodal foundation models that understand what happens in video and how visual information is organized across time and space. The Perception Models team develops general-purpose representations that work across full videos, clips, regions, and individual entities.
Rather than building a collection of narrow, task-specific models, we use signals from capabilities such as detection, segmentation, and tracking to strengthen shared video representations.
As a Senior ML Research Scientist, you will lead research at the intersection of spatiotemporal modeling and multimodal representation learning. You will define open-ended research problems, validate ideas through rigorous large-scale experiments, and turn promising results into production capabilities.
This role is ideal for a hands-on researcher with deep expertise in at least one of these areas and a strong interest in connecting them.
Research multimodal representations that connect temporal and spatial structure with semantic understanding.
Develop models that operate across multiple levels of granularity, from full videos and clips to regions and entities.
Design training objectives, datasets, evaluation methods, and large-scale experiments.
Use task-specific vision models as supervision or components, and integrate useful signals into general-purpose representations.
Partner with research and engineering teams to scale and ship new capabilities.
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.
A strong research track record in computer vision, video understanding, multimodal learning, or a related field.
Deep expertise in at least one of the following: video foundation models, self-supervised or contrastive learning, embeddings, and retrieval; or temporal modeling, object-centric learning, detection, segmentation, and tracking.
Strong hands-on skills in Python and PyTorch or an equivalent deep learning framework.
Proven ability to design and run rigorous experiments at scale.
Research impact through publications, production systems, or both.
The ability to own ambiguous problems and collaborate across research and engineering.
We evaluate based on relevant technical skills and sustained industry impact. This role is typically a strong fit for engineers with an MS and deep industry experience who have evolved from individual contributor to technical leader in production ML environments.
Experience with open-vocabulary vision or dense and region-level representations.
Experience with large-scale video training, data curation, or evaluation infrastructure.
Experience translating research into production.
Experience leading research projects or mentoring researchers.
This is an opportunity to define how a foundation model understands the same video across multiple levels of granularity, not simply improve a single task-specific benchmark.
Work Location: Seoul Itaewon office + Pangyo satellite office (Hybrid Based)
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