Member of Technical Staff, Applied AI
Listed on 2026-02-28
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Software Development
AI Engineer, Machine Learning/ ML Engineer, Data Scientist, Software Engineer
TL;
DR: We are seeing strong market demand and an aggressive 6-month product roadmap, so we are expanding our engineering team. We're looking for someone highly technical (our current team includes 3 IOI medalists) who wants to build a product that is changing how companies make decisions. If you're excited about tackling complex problems end-to-end, we should talk.
Listen Labs is an AI-powered research platform that helps teams uncover insights from customer interviews in hours — not months. We help customers analyze conversations, surface themes, and make faster, smarter product decisions.
Company highlights — entirely product-led:World-Class Team: Founded by serial entrepreneurs (previous AI exit), former co-founders, and talent from Jane Street, Twitter, Stripe, Affinity, Bain, Goldman Sachs, and many more Sequoia-backed startups (plus IOI/ICPC backgrounds).
Hypergrowth: We’re a 40-person team backed by Sequoia, growing from $0 to a $14M run‑rate in under a year. We move fast, care deeply about craft, and love working with people who take ownership.
Traction: Rapid growth across segments with enterprise wins at Google, Microsoft, Nestlé, and P&G.
Performance: Industry-leading win rate driven by a highly differentiated product.
Market Validation: Consistently winning customers across all segments with over six‑figure lands that lead to quick expansions.
Viral Product: Interviews are shared with tens of thousands of viewers, fueling PLG, organic expansion, and daily inbound from Fortune 500s.
McKinsey On Demand:
Building a research agent
Hiring McKinsey is different than buying software. You don't just get tools, but get opinions, experience, and execution. We build Listen with that perspective:
You have an AI agent on your side that knows everything about our platform and the best research practices. It helps you set up your project, conduct interviews with your goals in mind, and analyze thousands of responses.
Database of Humanity
One of the key value props is our ability to find the people you are looking for (eg, "power users of ChatGPT and Excel"). We are building a database of millions of humans. The more studies you do with Listen, the better we understand you. This enables finding people with unmatched accuracy and, in the long run, extrapolating what a person would say based on all previous conversations -- imagine answering questions for your best friend.
Realtime Video Interviews
The next version of our AI interviewer will have emotional understanding of video and voice to read between the lines. The goal is to make our interviewers more nuanced and effective than the most senior user researchers. This involves computer vision, speech analysis, and real‑time decision making.
Distributed Information Mining
The most interesting information is not publicly accessible on the web; it lives only in people's minds. We are building an agent that, given a question, finds the right people to talk to, asks the right questions, and returns a report and actionable recommendations. That's what consultants charge millions for. The ceiling is incredibly high, and we are pushing the technical boundaries to help companies, from investment firms to tech companies, make the best decisions.
Customer Preference Model & Synthetic Personas
We're bringing Jeff Bezos' vision of the customer being part of every life decision. We're building the most profound understanding of customers, which will allow us to extrapolate to new questions via synthetic personas. This involves complex modeling of human behavior, preferences, and decision‑making processes.
You want to solve problems end‑to‑end: Our team is split vertically, so every engineer owns a part of the product and needs to make decisions across the LLM pipeline, infrastructure, backend, and UX (with help!).
You have a high bar for quality: In a startup, moving fast is essential. But even more important is to care about your output, obsess about details, and build a product that works, especially in the time of AI. Slop compounds!
You want to push LLM capabilities: We continually push the most advanced AI models to their…
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