AI Product Engineer (P+E)

About the role

About Klarity

Tldr on us: Series B, $91M raised, 7x growth last year.

Digital Transformation gave rise to a $600B consulting Industry. AI Transformation will be 10x bigger and will be delivered through agents. We built that.

Our AI discovers how work actually happens across every team and application, Structures it into a living Context Graph, and Improves it continuously.

ServiceNow mapped 900+ processes in 9 days. DoorDash captured 3,800+ finance operations in 14 weeks. That's not a project. That's compounding intelligence.

OpenAI, Google, DoorDash, and Stripe use Klarity to transform how they transform. We shipped GPT-4 document chat within 12 hours of OpenAI's API launch.

We move fast, reward agency, and care deeply about our customers, our team, and our mission.

The Role

We're looking for an AI Product Engineer who's excited to work at the intersection of product thinking and technical execution. You may be early in your career, but you already have strong instincts for what makes a great product — and the technical chops to start building it. You'll work alongside senior engineers and product leaders, owning meaningful pieces of features from customer insight through production.

Who You Are

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Product thinker with engineering foundations — You have budding product instincts and a genuine interest in understanding users. Your technical background (CS fundamentals, React, TypeScript, Python) lets you prototype ideas and ship MVPs — you're building toward independence and learning fast along the way.

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Energized by customer discovery — You're genuinely curious about customer problems and eager to get in front of users. You're comfortable joining customer calls, asking thoughtful questions, and developing product conviction through real conversations.

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Strong UX sensibility and visual taste — You're not a trained designer, but you know what good UX looks like. You notice when flows feel off, care about spacing and hierarchy, and form strong opinions about how things should work.

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Exposure to AI-native product development — You've built projects using LLMs or AI APIs — whether in production, internships, or personal work. You're developing intuition for the product tradeoffs: how to scope around non-determinism, when AI adds genuine value versus complexity, and how to set user expectations for probabilistic systems.

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AI-native builder who moves fast — You use AI coding assistants like Cursor, Claude Code, and Replit to prototype rapidly and test product directions with real code. AI lets you validate 10 ideas in the time it used to take to build one.

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Own outcomes, not just features — You care about whether what you shipped actually worked successfully. You want to define success metrics, talk to users, analyze data, and iterate based on learnings — not just close tickets.