About Within

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 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.

What You'll Own

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.

What We're Looking For

7+ years in product at B2B SaaS or AI-platform companies, at staff-level scope. Cross-functional influence and organizational impact, with real exposure to information architecture, cross-product strategy, and complex enterprise stakeholders.

A systems thinker who works across the whole org. You see how a decision in one pod ripples into three others, and you design the process, context, and metrics that keep them coherent. You've imposed order on cross-team chaos before: prioritization frameworks, operating cadences, or context systems that made an org measurably faster.

A translator between GTM and engineering. You're as comfortable in a customer conversation or a QBR as in a spec review. You turn messy field input from Sales and Value Delivery into product signal, and product changes into rollouts that customers and internal teams can absorb.

A connector and a clear writer. You connect easily with people across very different backgrounds, from executives to salespeople to engineers, and you make excellent first impressions. You compress ambiguous customer, GTM, and stakeholder input into clear, prioritized direction, and you write the release comms, enablement, and prioritization narratives that align engineering, design, GTM, and executives. You push back with evidence, not authority.

Fluent in data and instrumentation. SQL, product instrumentation, and modern analytics and BI tooling. You cut through entropy to find the metrics that actually matter, and you've built dashboards people genuinely used. Note: this is not a data-platform architecture role; we're hiring for that separately.