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About Docket
Docket is the AI Marketing Agent platform helping B2B marketing teams move past the broken MQL model. Our agents engage buyers in real conversations, qualify intent using our proprietary Sales Knowledge Lake™, and surface Agent Qualified Leads (AQLs), a far better signal than form fills and lead scores.
We're building the category of agentic marketing. We need an Outcomes Architect to make sure every customer gets measurable pipeline, value and ROI from it.
Why this role exists
AI agents don't need adoption. Nobody has to be trained to log in, so the traditional CSM job of driving usage doesn't apply here.
What our customers need is a strategic partner. Someone who understands their business, their role and the problems they're trying to solve. Someone who helps them decide which outcomes matter, measures them, changes the approach when the numbers fall short, and knows where to double down when they work.
That's an Outcomes Architect. The role is closer to strategy consulting than account management, with real technical depth underneath. You'll own customers from pilot kickoff through renewal and expansion, and you'll be measured on the results they get.
What you'll own
Your customers, end to end. Typically 4–5 active pilots and 10–15 annual customers. You lead the pilot and stay with the account after it converts, so customers have one owner throughout.
The outcome, not the relationship. You define success with the customer, set the baseline, measure it, and own the results: pipeline, value and ROI.
Retention and growth. Renewals, expansion into new agents and use cases, and customer stories we can share.
What you'll do
Set the bar at kickoff. Agree on success criteria and a baseline with every customer, and deliver the business outcomes they bought Docket for, from pilot through renewal and expansion.
Design and tune the agents. Prompts, qualification logic, routing rules and agent behavior.
Connect the stack. Integrate agents with HubSpot, Salesforce, Clay, Gong, webhooks and APIs, and trace where leads get lost.
Find the signal. Analyze conversation and visitor data to see what's converting, what's leaking and what the market is asking about.
Drive the decision. Turn analysis into clear recommendations for CMOs and demand gen leaders, and walk them through what to change.
Grow the account. Spot the next agent or use case each customer needs, and build the business case for it.
Shape the product. Bring customer feedback to Product with evidence, not anecdotes.
Run on AI yourself. Build the workflows, prompts and automations that let you manage a full portfolio without drowning in it.
Who you are
You've been the customer. 4+ years in B2B marketing, demand gen, growth or marketing ops. You've owned pipeline, MQL or conversion targets, and you know what a CMO is measured on. A traditional CSM background isn't what we're looking for.
You own numbers. You can name a metric you were accountable for, where it started, where you took it, and what you changed.
You're AI-native, not just AI-comfortable. You've rebuilt a real process with AI, not just used it to write faster, and you can show us what you built.
You've built with conversational AI. Hands-on prompt engineering or conversational AI work, not just reading about it.
You're technical without an engineering title. You've built Zaps, Clay tables, HubSpot workflows or prompt chains because it was faster than filing a ticket.
You've worked at an AI-native or early-stage SaaS company. You know how to move fast when the playbook is still being written.
You've worked with US customers. You're comfortable running calls and building relationships with US-based marketing and revenue leaders.
You tell executives the truth. You can tell a CMO their agent isn't converting, back it with data, and come with a plan.
You thrive when the goal isn't fully defined. You can work out what success looks like with a customer after kickoff, then go deliver it.
Nice to have Experience selling or implementing marketing automation or martech
How we interview
Come ready to show something real you built with AI: a prompt, an agent, a workflow or an automation. Walk us through the problem it solves, how you built it, what broke along the way, and how you know it works. No-code builds are welcome. An engineering background isn't required.