Listenlabs

Listenlabs

Founding Research Scientist, Human Simulation

San Francisco, CA

Sponsorship not specifiedDetected 77 days ago
ReactLLMsAgentic AIResearch

About the role

  • We're the bridge between AI systems and what humans actually want.
  • What used to take research teams weeks per study, we do in hours.
  • Where it's going: every interview feeds a human preference model.

Responsibilities

  • TL;DR: Listen is building a human-preference model that companies and AI agents query to predict what people think, want, and decide.
  • We're hiring a founding researcher to lead our simulation initiative, the model that lets AI systems predict what humans would think, want, and decide.
  • As AI gets better at building things, the bottleneck shifts to knowing what to build.
  • 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.
  • You can train models, write evals, and collaborate with our research engineers to put the model into production.

Compensation

  • Top of market compensation with meaningful equity.

Benefits

  • Top of market compensation with meaningful equity.
  • Comprehensive healthcare and dental, flexible time off, a culture that values balance and trust.
  • This is how you share the roadmap and vision for this initiative.

Company info

  • Series B with $100M raised from Sequoia, Conviction, Ribbit, AI Grant, and Pear VC.
  • Selective team of <20 engineers including VC-backed founders, IOI medalists, and engineers from Jane Street and Tesla Autopilot.
  • Customers include Anthropic, Cursor, Perplexity, Google, Microsoft, Robinhood, Nestlé, P&G, and Sweetgreen.
  • Sequoia-backed, $100M raised, customers include Anthropic, Google, and Cursor.
  • Today our customers are companies.
  • Soon, AIs themselves will be our customers.
  • Our platform runs AI-moderated video interviews at massive scale.

This listing is sourced directly from Listenlabs's careers page and normalized into a canonical job model.