Head of Applications
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ABOUT THE ROLE
TwelveLabs spent years building the intelligence layer for video: shared infrastructure, multimodal video models, and an agent harness — the orchestration layer — that can understand and reason over video. Our vision for video superintelligence and pioneering work for large-scale video understanding allowed us to raise over $200M from Silicon Valley's best VCs like Index Ventures and NEA and leading enterprises like Nvidia and Amazon that are shaping the future of AI. The next chapter for TwelveLabs is to turn that foundation into products that people can use without needing to understand the technology underneath.
We are hiring a Head of Applications to build that layer.
You will decide where TwelveLabs should play, find the first use cases where our technology creates a step change in the user experience, and turn those insights into products customers use and pay for. You will own the application layer from customer discovery through product, engineering, launch, adoption, and growth.
This is a builder-GM role. It is not a conventional product management job, a growth marketing job, or a large-team executive role. You should be comfortable talking with customers in the morning, working through a product or technical tradeoff in the afternoon, and assembling an exceptional team for the 0-to-1 journey that evening. Ideal candidates will come with 12+ years of experience across product and product leadership roles, preferably at early to mid stage software companies.
IN THIS ROLE, YOU WILL
- Set the strategy for TwelveLabs' application layer: which users and workflows to serve, what to build, and what not to build.
- Find a focused wedge through direct customer discovery, rapid prototyping, and disciplined market testing.
- Ship opinionated, AI-native applications for non-technical users on top of TwelveLabs' core models, agent harness, and shared infrastructure. Define application-specific evals, task-success criteria, reliability thresholds, human review, and launch guardrails.
- Own the full outcome of the applications you ship, including activation, engagement, retention, monetization, and customer value. Shipping is the start, not the finish.
- Build a fast learning loop between users and the product. Turn usage data and customer behavior into better workflows, interfaces, and model experiences.
- Act as the demanding internal customer for our platform and model teams. Define the capabilities, APIs, latency, quality, and reliability the application layer needs without rebuilding the layers below it.
- Partner closely with Design, Research and Engineering, Product, Commercial, Revenue, and Partnerships to take products from prototype to repeatable adoption. Work deeply with lighthouse customers, while converting what you learn into reusable products rather than one-off implementations.
- Recruit and lead a lean and extremely high-performing team with the range to move from idea to production.
- Establish a culture of speed, craft, direct user contact, and clear accountability. Keep the team lean as the product and business grow.
You May be a good fit if you have
- You have built and grown a product from zero to meaningful adoption. You can explain what users did, what changed because of your decisions, and where you were wrong.
- You have exceptional product judgment and know what great looks like. You can find the narrow workflow that matters, reduce a complex technology to a simple experience, and say no to attractive distractions.
- You are technically fluent enough to work directly with engineers and researchers on architecture, model behavior, latency, evaluation, reliability, and cost. You do not need to be the strongest engineer in the room, but you cannot treat the technology as a black box.
- You understand the realities of AI-native products: probabilistic behavior, evaluation design, trust, human review, privacy and rights constraints, and the tradeoffs between quality, latency, and cost.
- You understand growth as part of the product. You have designed or led loops that improved activation, retention, distribution, or monetization, not just acquisition campaigns.
- You recruit well and have built an early team of unusually strong, low-ego people. You know how to operate before every function has its own leader.
- You are close to the work. You use the product, talk with users, inspect the data, and raise the quality bar through direct involvement.
- You move quickly without confusing activity with progress. You create evidence, make decisions, and change course when the evidence says you should.
- You communicate clearly across research, engineering, design, and go-to-market teams and can resolve cross-layer tradeoffs without creating organizational drag.
WHAT SUCCESS LOOKS LIKE IN THE FIRST 90 DAYS
- Build a clear view of the most promising users, workflows, and distribution paths for the applications built.
- Narrow the field to one or two application wedges using an explicit market-attractiveness and right-to-win filter, with clear reasons to reject the rest.
- Put working prototypes in users' hands and establish a weekly build-measure-learn cadence.
- Establish application-specific evals and define the quality, trust, latency, and cost thresholds required for production use.
- Define the boundary and operating rhythm between the application team and the shared infrastructure and model teams.
- Close the first critical hires for the applications team.
In the first 3 months
- Launch a real product to a focused set of users, not just a demo or technology showcase.
- Show evidence of repeated use and measurable customer value in the chosen workflow.
- Establish instrumentation for activation, engagement, retention, quality, and cost to serve.
- Demonstrate measurable user ROI, such as time saved, content found, decisions accelerated, or workflows completed.
- Turn early customer-specific work into reusable workflows, templates, integrations, or application capabilities.
- Translate application needs into a prioritized set of improvements for the core models, agent harness, and shared infrastructure.
- Build a small team that can discover, build, launch, and iterate without heavy coordination overhead.
In the first 6 months
- Establish at least one application with a credible path to product-market fit and durable distribution.
- Turn early adoption into repeatable growth and monetization.
- Create a clear portfolio thesis for what TwelveLabs should build next, based on what we have learned rather than top-down speculation.
- Make the application layer a compounding advantage for the entire company by sharpening our platform, models, customer understanding, and go-to-market motion.
BACKGROUNDS THAT COULD FIT
There is no single required path. You may be:
- A product leader who has repeatedly shipped zero-to-one products and stayed accountable for growth after launch.
- A product growth leader with strong technical depth and a record of changing the product, not only optimizing funnels
- A growth or product engineering leader who became the owner of a product and business outcome.
- A founder or early startup leader who found a wedge, shipped the product, won the first users, and built the first team.
What matters is the combination: product taste, technical fluency, growth instinct, and the ability to build a team and business from a blank page.
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Twelve Labs
View Company ProfileTwelve Labs is a pioneering artificial intelligence company that specializes in building foundation models for multimodal video understanding. Founded in 2021, the San Francisco-based company is on a mission to give machines the ability to see, hear, and reason about video content exactly like humans do. Under the hood, Twelve Labs utilizes its powerful proprietary AI models—such as Marengo (a video encoder) and Pegasus (a video-language model)—to simultaneously process temporal and spatial data. Instead of relying on basic manual tags or metadata, the platform automatically extracts and maps relationships between visual actions, spoken words, on-screen text, and audio cues, turning massive video libraries into mathematically searchable embeddings. Their primary target audience includes global media and entertainment conglomerates, advertising agencies, and enterprise security teams who need to instantly search, summarize, and analyze petabytes of raw video footage through simple API calls. What sets Twelve Labs apart in the generative AI landscape is its highly scalable, video-native architecture that brings advanced, context-aware visual intelligence to any workflow, allowing developers to build sophisticated applications without having to train and maintain complex multimodal infrastructure themselves.
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