Listen Labs
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Member of Technical Staff
role at
Listen Labs
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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.
Background Write code and talk to customers. That's the YC secret on building a great company. Writing code is getting easier, so talking to customers becomes even more critical.
That's what Listen does. It's an AI that talks to hundreds of your customers per week and tells you how to improve your product or messaging. Listen handles:
Designing the interviews.
Finding the right participants.
Analyzing the responses.
Fortune 500 companies spend $100M+/year on consultants to do just that.
Our customers, including Google, Microsoft, Anthropic, Canva, Sony, SKIMS, and Nestlé, have realized millions in ROI through improved products, advertising, and strategic decisions.
Technical Challenges
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.
Team The engineering team includes:
Previous companies are from Jane Street, Tesla Autopilot, and affirm
We are growing aggressively in the following months
What We Look For
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 limits and work with the foundational companies on their new releases.
You are a clear thinker and communicator:
We only have one meeting a week and expect you to communicate tradeoffs, problems, and blockers directly.
You are highly technical:
Most of our team has started coding as young teenagers and nerd out on details from language design to compilers.
Life at Listen Labs
Competitive Compensation:
We're backed by world‑class investors, including Sequoia Capital, Conviction, AI Grant, and Pear VC, and offer competitive compensation packages with meaningful equity ownership.
Over $30B in market cap has been created in adjacent industries (Medallia, AlphaSense, GLG, Ipsos, Kantar). Our Sequoia partner, Bryan Schreier, was the first investor in Qualtrics—a $12B company tackling similar problems to ours.
Benefits that Support You:
Comprehensive healthcare and dental coverage, flexible time off to recharge, and an environment that values balance and trust.
Room to Grow:
As an early member of the team, you'll have the opportunity to take on new responsibilities, shape processes from scratch, and grow alongside the company. We value people who want to stretch beyond their role and build something lasting.
Compensation Range $150K - $300K
#J-18808-Ljbffr
Member of Technical Staff
role at
Listen Labs
Get AI‑powered advice on this job and more exclusive features.
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.
Background Write code and talk to customers. That's the YC secret on building a great company. Writing code is getting easier, so talking to customers becomes even more critical.
That's what Listen does. It's an AI that talks to hundreds of your customers per week and tells you how to improve your product or messaging. Listen handles:
Designing the interviews.
Finding the right participants.
Analyzing the responses.
Fortune 500 companies spend $100M+/year on consultants to do just that.
Our customers, including Google, Microsoft, Anthropic, Canva, Sony, SKIMS, and Nestlé, have realized millions in ROI through improved products, advertising, and strategic decisions.
Technical Challenges
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.
Team The engineering team includes:
Previous companies are from Jane Street, Tesla Autopilot, and affirm
We are growing aggressively in the following months
What We Look For
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 limits and work with the foundational companies on their new releases.
You are a clear thinker and communicator:
We only have one meeting a week and expect you to communicate tradeoffs, problems, and blockers directly.
You are highly technical:
Most of our team has started coding as young teenagers and nerd out on details from language design to compilers.
Life at Listen Labs
Competitive Compensation:
We're backed by world‑class investors, including Sequoia Capital, Conviction, AI Grant, and Pear VC, and offer competitive compensation packages with meaningful equity ownership.
Over $30B in market cap has been created in adjacent industries (Medallia, AlphaSense, GLG, Ipsos, Kantar). Our Sequoia partner, Bryan Schreier, was the first investor in Qualtrics—a $12B company tackling similar problems to ours.
Benefits that Support You:
Comprehensive healthcare and dental coverage, flexible time off to recharge, and an environment that values balance and trust.
Room to Grow:
As an early member of the team, you'll have the opportunity to take on new responsibilities, shape processes from scratch, and grow alongside the company. We value people who want to stretch beyond their role and build something lasting.
Compensation Range $150K - $300K
#J-18808-Ljbffr