Customer research conducted, analyzed, and summarized by AI. Backed by Sequoia, AI Grant and Pear VC.
About the role
TL;DR: We're hiring engineers who can build a complex AI-native product on a small team of former founders and top-tier builders. Human API. A model of millions of humans is only useful if you can call it from where decisions happen. We want to embed this into Slack, Linear, IDEs, and coding agents themselves. Imagine an agent shipping code, asking Listen what humans actually want, taking action, and iterating.
What they're looking for
- You solve problems end to end. The team is split vertically, so every engineer owns a part of the product and makes decisions across the LLM pipeline, infrastructure, backend, and UX
- You're a future or past founder. You scope your own work, think about the customers, and own your decisions
- You care about getting things right. Moving fast is essential, but a 100% solution is much more powerful than an 80% one. When something breaks, you go to root cause
- You're excited about pushing LLMs to their limits. We work directly with the frontier model labs on new releases and constantly probe where they break
- You communicate complex ideas in writing. We work independently with one meeting a week, so writing is how tradeoffs, problems, and decisions get worked through together
- You're highly technical. Most of our team started coding as teenagers and nerd out on details from language design to compilers
More about this role
TL;DR: We're hiring engineers who can build a complex AI-native product on a small team of former founders and top-tier builders.
As AI gets better at building things, the bottleneck shifts to knowing what to build. We're the bridge between AI systems and what humans actually want. Today our customers are companies. Soon, AIs themselves will be our customers.
Our platform runs AI-moderated video interviews at massive scale. We find the right people from a network of millions, our AI conducts open-ended conversations with thousands of them in parallel, and we surface what to build next. What used to take research teams weeks per study, we do in hours.
Where it's going: every interview feeds a human preference model. We simulate human behavior at scale: how people react to new ideas, how they make decisions, how preferences shape markets, and how change ripples through society. We expose this as the Human API. An AI agent writes code, asks Listen whether users would actually want a feature, gets a grounded answer back, and iterates. Closed-loop product development at AI speed. Every coding agent will eventually need this signal.
World-Class Team : Founded by serial entrepreneurs —...
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