Customer research conducted, analyzed, and summarized by AI. Backed by Sequoia, AI Grant and Pear VC.
About the role
Securing the Database of Humanity. We hold voice, face, and device-linked profiles for millions of people. You'll help design the protections that data deserves: encryption, access boundaries, retention, and abuse resistance, built into the product rather than bolted on. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- You build the fix, not just the finding. You're not a security analyst writing tickets, and you're not a pen tester delivering a report. You close the loop by shipping the guardrail, the library, the pipeline check, or the patch
- You're curious about AI security. You're excited to learn how LLM systems fail and to help define best practices in a field that's still being written. Prior LLM security experience is a plus, not a requirement
- 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 communicate complex ideas in writing. We work independently with one meeting a week, so clear writing is how you'll make the case for tradeoffs, explain risk to engineers, and get fixes prioritized
- You solve problems end to end. You scope your own work, think about customers and their trust, and own your decisions
More about this role
TL;DR: We're hiring an engineer who builds security into a complex AI-native product, not one who audits it from the outside. You write real code, ship real fixes, and understand how systems are built well enough to know exactly how they break.
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, exposed to the world through the Human API. AI agents will call Listen from Slack, Linear, IDEs, and coding agents to ask what humans actually want. The trust people place in that signal is everything, and security is how we earn it.
World-Class Team: Founded by serial entrepreneurs, Alfred (Bemlo, YC W22) and Florian (IOI medalist, ICPC World Finalist),...
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