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

Stripe is building a dedicated Professional Services practice for the world's leading AI companies. We work with AI labs, infrastructure providers, and application companies that launch quickly, change their business models often, and expand globally early. Their work with Stripe spans products, regions, and technical teams, creating some of our most important implementation challenges.

As an Integration Engineer, you will work directly with these companies' engineering teams to move complex implementations, migrations, product launches, and international expansions into production. You will also turn what you learn into reusable code, tools, and delivery methods for the wider AI segment.

Responsibilities

• Plan and execute integration engineering end to end, from architecture through code implementation, testing, launch, and stabilization.
• Lead discovery with users, understand their goals and systems, and translate complex use cases into reliable Stripe integration designs.
• Design and build integrations, connectors, migration tooling, automations, and other technical components needed to reach production.
• Act as an ongoing technical partner to users' engineering teams, explaining Stripe's products and API architecture while managing dependencies and trade-offs.
• Investigate and resolve difficult integration issues by inspecting code, reproducing behavior, testing functionality, and coordinating across Stripe teams.
• Lead hands-on-keyboard work where appropriate, including proof-of-concept implementations, code reviews, testing strategies, CI/CD guidance, and architecture recommendations.
• Review existing integrations and recommend improvements to reliability, performance, maintainability, and readiness for growth.
• Use AI-assisted engineering and delivery tools to reduce manual work and improve the speed and quality of implementation.
• Partner with Product and Engineering to identify recurring user needs and opportunities to improve Stripe's products, APIs, and tools.
• Create reusable technical assets, including code, reference architectures, migration methods, guides, dashboards, and data analysis.

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.

Minimum requirements

• 6+ years of experience in software engineering, integration engineering, solutions engineering, technical consulting, or a comparable customer-facing engineering role.
• Ability to write code in languages such as Go, Java, Python, C#, C++, or similar, and quickly understand and debug unfamiliar code.
• Strong generalist technical experience, including SQL and an understanding of APIs, data structures, distributed systems, and software architecture.
• Experience designing or delivering integrations between complex systems and third-party APIs.
• Understanding of API security, reliability, testing, and production-readiness practices.
• Ability to move between architecture and implementation, using code, logs, and data to solve open-ended problems.
• Ability to explain technical concepts clearly to engineers, business stakeholders, and senior leaders.
• A user-first approach and confidence leading technical work across multiple teams in an ambiguous, fast-moving environment.

Preferred qualifications

• Experience with AI-native companies, developer infrastructure, cloud platforms, or other rapidly scaling technology companies.
• Experience with payments or financial infrastructure.
• Experience using AI coding assistants or LLM-based agents in engineering work.
• Experience delivering migrations, multi-product implementations, or international launches.
• Strong technical writing skills and a willingness to teach and mentor others.