Founding Research Scientist, Human Simulation

Job description

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. BACKGROUND 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. COMPANY HIGHLIGHTS - World-Class Team: Founded by serial entrepreneurs — Alfred (Bemlo, YC W22) and Florian (IOI medalist, ICPC World Finalist) — and built with talent from Google, Meta, Tesla, Amazon, Jane Street, and X, alongside Bain, Goldman Sachs, McKinsey, and Sequoia-backed startups like Applied Intuition, Scale AI, and Rippling. Over 30% of our team are former founders, and multiple are competitive programming medalists. - Hypergrowth: Since launching, Listen has grown annualized revenue 15x and completed over 2 million interviews — backed by $100M in total funding led by Ribbit Capital, with Sequoia, Conviction, and Pear VC participating. - Traction: Already serving 15% of Fortune 100s. Enterprise wins and expansions across Anthropic, Microsoft, Nestlé, Perplexity, Sweetgreen, and more — with teams telling us research that used to take six to eight weeks now happens in days. RESEARCH CHALLENGES - 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? WHO YOU ARE - 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. LIFE AT LISTEN LABS - Health, covered: Full medical, dental, and vision. FSA/HSA and life insurance are available too. - Competitive Compensation: Backed by world-class investors including Sequoia, Ribbit Capital, Conviction, and Pear VC — we hire the best and treat them accordingly, with meaningful equity ownership. - Annual Learning Stipend: Books, courses, conferences, coaching, language lessons — almost anything that makes you sharper. - Annual Wellness Stipend for your health and headspace: Gym memberships, race fees, massages, ergonomic gear, and more. - Monthly Stipend for AI tools: A dedicated budget for the software and GPU credits that make you faster. - Fed, daily: A private chef serves lunch and dinner in our SF office; NYC and London teams get daily meal credit. - Flexible time off: A take-what-you-need vacation policy. - We celebrate together: Company offsites (Hawaii this Summer!) and events like our annual holiday party — every win is a team win. - Room to grow: As an early member of the team, you'll own end-to-end processes from scratch and grow alongside the company.