Lead Product Manager, Embedding & Search

Who we are

At Twelve Labs, 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 remarkable $107 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.

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.

Video is the richest and most complex data type in the world. Twelve Labs builds the foundation models and products that give machines genuine understanding of what is happening inside it.

Marengo is our multimodal video embedding model. Search is the product built on top of it. They are the technical center of the platform: what customers deploy in production, what competitors are trying to replicate, and where some of the hardest product decisions live.

You will own both.

You set the strategy and roadmap for Marengo and Search. You work with the research team on what the model should learn, how to evaluate it, and when it is ready to ship. You work with customers and field engineers to understand where retrieval breaks in production and what they will need six months from now.

Your week splits roughly three ways: research partnership, customer and field work, and internal product execution. The role requires real depth in all three, not fluency in one with awareness of the others.

The scope is the full stack: evaluation data definitions, model evaluation, release cadence and management, ranking quality, the search API, and deployment across managed SaaS, customer hosted environments, and AWS Bedrock. Multimodal video retrieval is becoming an industry assumption. You will be the person deciding how Twelve Labs stays ahead of that curve.

This role is hybrid in San Francisco with two days onsite per week. Due to daily collaboration with our research team in Seoul, we expect availability until approximately 8 p.m. PT on most weekdays;
Fridays are an exception.

• Set the product strategy and roadmap for Marengo and Search, deciding what gets built, what gets deferred, and what gets killed.

• Partner with the Marengo research team on model quality: eval rubrics, training data investments, release readiness.

• Partner with the GTM on launch planning, execution, and enablement, including post‑launch monitoring.

• Spend real time with customers and field teams, understanding where retrieval fails in production and anticipating what they will need next.

• Define the quality bar for retrieval and hold it across every release and every deployment shape.

• Own how embeddings and search get deployed across managed SaaS, customer hosted environments, and AWS Bedrock.

• Stay sharp on the competitive landscape.

• You have a research, ML, or engineering background with real work in retrieval, embeddings, vector search, or multimodal models, and you moved toward product because you care more about what gets built and why.

• You have been a senior solutions engineer or forward‑deployed engineer with deep ML understanding, and you have been the de‑facto product owner on the hardest customer problems, whether or not the title was yours.

• You can go deep on retrieval architecture tradeoffs with a researcher in the morning and frame a…