Lead Product Manager, Embedding & Search

TwelveLabs; San Francisco (CA); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Flexible PTO

Parental leave

Collaborative work environment

Job summary

TwelveLabs in San Francisco is seeking a Product Manager to lead the strategy and roadmap for Marengo, their multimodal video embedding model. The role involves deep collaboration with research teams and direct engagement with customers to ensure the product meets their needs.

The ideal candidate has a strong background in research or machine learning, with experience in product management. This hybrid role offers the opportunity to influence the development of cutting-edge AI technology.

Qualifications

• Research background in retrieval or embeddings.
• Experience as a senior solutions engineer or product owner.
• Ability to engage deeply in technical discussions.

Responsibilities

• Set product strategy for Marengo and Search.
• Collaborate with research team on model quality.
• Work with customers to understand retrieval needs.

Skills

Research background

Experience in ML

Product strategy formulation

Customer engagement

Deep ML understanding

Strong opinions on search production

Job description

About the Role

Video is the richest and most complex data type in the world. TwelveLabs 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 TwelveLabs 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 8pm PT on most weekdays, Fridays are an exception.

In this role, you will

• 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 may be a good fit if you have

• 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 product decision for a GTM team in the afternoon, and both conversations are substantive
• You have strong opinions about what makes search work in production and can back them with evidence, not intuition
• You have strong opinions on how to best serve humans and agents as distinct customer segments
• You see what customers need today and can extrapolate what they will need next. You use current demand as a foundation for roadmap decisions, not just a backlog.
• You have shipped product with strong enterprise and PLG (Product Led Growth) motions attached

Preferred Qualifications

• 5 to 8 years of experience, though what matters is demonstrated capability, not tenure
• 3+ years of shipping products with a model related core
• Time at a company where embeddings, vector search, or retrieval was integral to the core product
• Experience with multimodal models and the operational cost of running them at scale
• Experience in video language models
• Experience augment product development and releases with modern AI tooling
• A large bonus if you have working fluency in English and Korean

Benefits and Perks

An open and inclusive culture and work environment.

Work closely with a collaborative, mission-driven team on cutting-edge AI technology.

Extremely flexible PTO and parental leave policy. Office closed the week of Christmas and New Years.