Knoetic

AI Pod: Fullstack Product Engineer

New York City, New York, United StatesFull timePosted 12 days ago
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✨ TLDR ✨

Join Knoetic ($50MM+ raised from Tier 1 VCs like Accel + Menlo + 100s of angels) as we launch an incredible series of new AI products.

1. You’ll join a newly-formed, high-intensity AI product pod. You’re not joining a well-oiled machine; rather, you’re going to help us build the machine in this elite AI pod.

2. You'll own features end-to-end as an IC, ship at incredible velocity, and have direct impact on a product used by 1000s of C-level executives from Box, Brex, Notion, OpenAI, Figma, and more.

3. You must be an AI-native engineer who’s customer-centric. That means (1) you’re easily a top 10% user of tools like Cursor, Claude Code, Codex, and more, and (2) you love creating delightful moments for our customers, whatever it takes.

❤️ KNOETIC AI + OUR CHIEF PEOPLE OFFICER NETWORK ❤️

Our mission is to build the “Second Brain” for every Chief People Officer & their HR team. We're backed by $50MM from Accel, EQT, Menlo, and 100s of legendary angels (Adam Grant, Shreyas Doshi, etc).

We're a beautiful blend of consumer + enterprise product that serves 1000s of C-level execs from companies like Box, Figma, dbt labs, Lyft, Notion, and OpenAI.

You can learn more about our “right brain” (CPOHQ) https://www.cpohq.com/ and ”left brain” (AI & People Analytics) https://www.knoetic.com/people-analytics here.

✨ OVERVIEW ✨

We're looking for a Fullstack Product Engineer who wants to:

- Own features end-to-end, from deeply understanding the customer problem to shipping the feature and driving its usage & adoption.

- Work across the entire stack (React, TypeScript, NestJS, PostgreSQL).

- Ship fast & high quality; you believe speed vs. quality is a false choice https://www.rippling.com/blog/what-it-takes-to-build-an-11b-startup-an-interview-with-parker-conrad-ceo-of-rippling, and in fact you can increase both in lockstep.

- Work directly with customers: from hopping on calls, to debugging their problems, and then iterating based on real feedback

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