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Klarity
San Francisco, California, United States
Source: Klarity careers · View original posting
From Klarity's posting. “We” and “our” refer to the employer.
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 Within 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 Opportunity
Enterprises are trying to transform themselves with AI, and most are failing at the same step: making AI actually useful inside real, messy workflows. That is the problem Within exists to solve, and it is the single biggest bottleneck in enterprise today.
As our product org scales, the constraint is no longer ideas. It's orchestration: getting the right context to every pod, measuring what we ship, and keeping GTM, Value Delivery, and customers moving in lockstep with engineering. This role owns that connective layer. Your job is to make the entire product org faster, better informed, and more decisive.
The agentic operating system for product and engineering.
Define and delegate work across multiple product pods, run an AI-native product-ops motion, and build the agentic loops and context flows, our own Company Brain turned inward on the product org itself, that keep pods moving without stepping on each other, and increasingly, let agents pick up pieces of that work directly.
Prioritization and resource allocation.
Continually answer "are the pods picking up the right priorities?" and reallocate when they aren't.
The context-and-metrics layer.
Dashboards and monitoring of customer usage and engineering productivity alike, with qualitative and quantitative signal combined continuously, through the same agentic flows you build, so every pod makes higher-quality decisions.
The GTM, engineering, and customer communication loop.
Release communications, rollout and enablement for GTM and Value Delivery, and co-ownership of customer-facing product documentation.
Product signal for the rest of the company.
Get usage insight to the teams who can act on it, for example notifying an AE the moment a customer starts using MCP, and give leadership a clear, current read on what's shipping, what's landing, and what's stuck.
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