Forward Deployed AI Accelerator, Marketing
StripeWho we are
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions of companies — from the world's largest enterprises to the most ambitious startups — use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.
About the team
The Stripe Marketing organization is undergoing a fundamental transformation in how it works. We're building a team of Forward Deployed AI Accelerators (FDAs) embedded directly with marketing cohorts to make AI the default mode for all work — not an occasional tool, but the foundation of how every marketer at Stripe executes.
Today, marketers are already achieving remarkable results — building their own data dashboards, creating agents that compress multi-day processes, and authoring tools that accelerate workflows. The FDA team exists to take what's working and systematically scale it across the entire marketing organization.
What you'll do
As a Forward Deployed AI Accelerator, you'll be embedded with a group of approximately 20 marketers organized by functional team, shared workflow, or location. You'll build alongside them, and you'll help them fundamentally change how they operate. Your measure of success is the number of workflows you've permanently transformed and the extent to which those in your group start any task with an AI tool.
Responsibilities
- Identify and document the highest-leverage workflow transformations within your group's day-to-day work by deeply understanding their processes and outputs • Build custom tools, agents, automations, and skills tailored to each marketer's specific responsibilities
- Coach and partner with each marketer through a progressive journey — from awareness, to first win, to regular AI integration, to full workflow transformation, to self-sufficiency • Teach marketers to build and iterate on their own tools over time, creating independence
- Recognize patterns across your cohort and systematically scale what works — a tool built for one marketer will be reusable for their peers
- Document every tool, playbook, and transformation pattern you build so the entire FDA team uses each other's work
- Share wins visibly within your cohort and with leadership to build momentum, inspire, and celebrate success
- Track individual and cohort progress rigorously against a defined maturity model, moving every marketer toward self-sustaining, AI-first work
- Prepare marketers for an agentic future — not just prompt writing, but designing, building, and overseeing autonomous, multi-agent workflows
Who you are
We're looking for people who have already lived the trans
Opens the company's application page
Listed via
Findwork
findwork.dev
Similar roles

Data Analyst
Harnham - Data & Analytics Recruitment

Senior Data Analyst
Harnham - Data & Analytics Recruitment

Service Charge Data Analyst
Robertson Bell
Data Analyst
R3vamp Limited
Design & Tech
Related reads from TCHNX

Why AI Design Tools Are Quietly Replacing Junior Designers and What Actually Comes Next
AI tools promise efficiency, but London studios are discovering an unexpected paradox: automation creates new bottlenecks requiring precisely the expertise being eliminated. We investigate what's actually happening to entry-level design work.

The Inference Economy: Why AI’s Biggest Cost Shift Is Happening After Training
A major shift in AI economics is reshaping the industry. As training frontier models becomes more expensive and inference becomes dramatically cheaper, companies are being forced to rethink how they build, deploy, price, and monetise intelligent systems.

The Emergence of Small Language Models: Why Efficiency Is Overtaking Scale
As the AI industry confronts computational costs and environmental concerns, a new generation of compact models is proving that bigger isn't always better. Small language models are reshaping enterprise AI deployment.