Founding Research Scientist, Human Simulation

TL;DR: Listen is building the human layer of AI: a preference model trained on millions of real human conversations. 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. Sequoia-backed, $100M raised, customers include Anthropic, Google, and Cursor.

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.

• 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.

• Post-PMF growth: 20x year-over-year revenue.

• Huge market: clear path to $1M+ contracts at over 50% of the Fortune 2000.

Modeling Humans. What does it take to actually understand a person? Which questions yield the most signal, how do we combine long-form interviews, demographics, and behavior into a useful model, and how do we predict a specific person's response to a question they've never been asked? Can we estimate how confident we are in a prediction?

Multi-Agent Dynamics. People don't form opinions in isolation. They influence each other, deliberate, and shift in groups. Can we simulate cohorts of synthetic humans deliberating, reaching consensus, or splitting into camps?

Generalization and Active Learning. With millions of interviews, how can we learn from patterns across people, contexts, and questions? When the model is uncertain, how do we go back to real humans to update the model?

• You have a strong research track record. Published work in LLMs, post-training, RLHF, behavioral modeling, simulation, or adjacent fields. Or equivalent industrial impact at a frontier lab.

• You pick the problems. This is a founding research role. You'll define what's worth working on, scope research programs, and decide what success looks like.

• You're genuinely curious about humans. You want to understand what people actually want, how they decide, and why preferences shift.

• You make research real. You can train models, write evals, and collaborate with our research engineers to put the model into production.

• You communicate complex ideas in writing. This is how you share the roadmap and vision for this initiative.

• Top of market compensation with meaningful equity.

• Comprehensive healthcare and dental, flexible time off, a culture that values balance and trust.

• Joining at an inflection point. PMF is real, the market ahead is enormous, the team is still small enough that your work directly shapes the company.