About the team

The Forward Deployed Engineering (FDE) team exists to solve the hardest problems our most complex enterprise users face, and to turn those solutions into platform capabilities that scale to everyone. We work across Revenue Suite, Payments, and adjacent products. This is not a support function. It is an engineering team that sits at the boundary between Stripe's platform and the real-world complexity of how large enterprises actually use it.

The traditional FDE playbook, one engineer per user who manually rebuilds context every rotation, is how every infrastructure company has done it for a decade. It does not scale. We are building something different: an agent-augmented model where AI handles the context-heavy, structured work (integration mapping, state reconciliation, infrastructure shadow testing, diagnostics) and human FDEs focus on the parts that require engineering judgment, product thinking, and direct user relationships. The goal is to build engineering muscle that lets us serve every strategic user through the platform, not just the handful we can staff individually.

We've proven the approach with some of the world's most demanding enterprise users, delivering measurable auth rate improvements, unblocking critical platform migrations, closing multi-product integration gaps, and driving real revenue. Now we're scaling the model. If you want to solve hard user problems, build reusable platform solutions, and help shape how forward deployed engineering works as a discipline, this is the team.

• What you’ll do
• As an Engineer on the RFA Forward Deployed Engineering team, you will:

Work alongside AI agents to serve users at scale. This is central to how our team operates. AI agents maintain real-time integration maps, run shadow tests against user setups, and perform automated state reconciliation between Stripe and user systems. Your job is the work that requires an engineer: making judgment calls on ambiguous problems, building relationships with user engineering teams, making product decisions, and designing solutions. You will also help shape the tooling itself, identifying what should be automated and what needs to stay human.

Engage directly with users. You will work with Stripe's most strategic enterprise users to understand their revenue, billing, and payments requirements. You translate what you learn into technical solutions, and you bring that user reality back to product teams. This is a genuinely user-facing role, not user-facing in the "I read a dashboard" sense.

Build across product boundaries. You will design and deploy products and solutions that address product-market fit gaps, not just in Billing but across multi-product boundaries (Payments + Invoicing + Global LPMs). You embed within Stripe product engineering teams to co-develop the highest-leverage capabilities.

Build reusable solutions, not one-off fixes. Contribute to a customization framework for RFA and adjacent products: tailored billing logic, financial workflows, integrations (custom metering, product catalog integrations, checkout flows). Build patterns and blueprints that scale beyond the individual engagement.

• Provide architectural guidance. Review user architectures, advise on best practices, and optimize integration and performance for complex enterprise environments. Contribute to a growing library of architectural patterns for the field.
• Resolve critical technical challenges. Diagnose and fix complex product/engineering problems across the stack. Identify systemic improvements that prevent recurrence and improve platform stability.
• Inform the product roadmap. The integration gaps, migration friction, and multi-product failures you surface directly shape Stripe's product strategy. You advocate for what users actually need based on what you've seen firsthand.
• Raise the bar on engineering. Improve engineering standards, tooling, and processes within the team. Help build for sustainability as the team grows.
• Who you are

You're a strong engineer who also thinks like a product person. You're comfortable with ambiguity, you'd rather talk to a user than read a spec, and you care about whether the thing you built actually solved the problem. Think: early engineer at a startup who played PM, did product discovery, partnered with users, and shipped. You're also interested in working at the frontier of agent-augmented engineering, not just using AI tools but helping define how an engineering function operates alongside them.

• We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
• 5+ years of experience in software engineering, with a strong focus on backend systems.
• Proven ability to design, build, and maintain highly available, scalable, and secure systems.
• Strong command of distributed systems, API design, and data modeling.
• Excellent problem-solving skills and the ability to quickly grasp complex technical and business domains.
• Clear communicator, both written and verbal, with technical and non-technical stakeholders including external users.
• Track record of working well in collaborative environments with product managers, TPMs, and other engineers.
• Willingness to engage directly with users to understand requirements and co-develop solutions. This is a core part of the role.
• Willingness to travel (~20-30%) to Stripe sites (Toronto, SF, Seattle) and user locations.
• Experience with financial automation or billing products (e.g., Stripe Billing, Tax, Revenue Recognition, or similar).
• Experience with multi-product integration: stitching together payments, invoicing, billing, and related systems.
• Familiarity with extensibility models, custom solution frameworks, or platform development.
• Experience working with large enterprise users or in a customer-facing engineering role.
• Prior experience in a fast-paced, ambiguous environment where priorities shift based on user needs.
• Interest in or experience with AI-augmented engineering: working with AI agents, LLM-powered tooling, or building workflows where humans and AI collaborate.