Staff Engineer, Payment Intelligence at Stripe in Seattle, WA

The Payment Intelligence group is responsible for optimizing each of the billions of dollars of transactions processed by Stripe each year on behalf of our users, in order to maximize successful transactions while minimizing payment costs and fraud. We own products like Radar from end to end and work across the technical stack: from machine learning over our users’ data, to integrating ML intelligence, building performant and scalable systems and services, serving real-time predictions as part of Stripe’s payment infrastructure, to building user-facing product surfaces like dashboards for insights, and controls to optimally manage users’ business. We’re looking for people with a strong background and passion in building successful backend systems, services and APIs that deliver impactful product values to our customers. You are comfortable in dealing with changes. You love to take initiatives, and bias towards action.

You will:

• Create long term technical vision for the org, and identify paths to deliver value in shorter term phases
• Architect and design systems with cross team and cross org impact
• Design, build, and maintain scalable, reliable and performant services and systems
• Make significant hands-on contributions to deliver critical projects and bring value to customers
• Lead by example to uphold high engineering standards, and elevate quality and engineering efficiency across Stripe
• Collaborate with stakeholders across the organization including dependency engineering teams, product, design, infrastructure, and operations
• Mentor engineers in their technical careers to help them grow

We’re looking for someone who has:

• At least 7 years of professional software development experience
• Strong expertise in large scale distributed system designs
• Strong experience in successful delivery of high quality production services and systems
• Experience in technically leading complex projects that involve multiple engineering teams
• Demonstrated experience in upleveling engineering best practices and creating technical efficiencies across teams
• Thrive in a collaborative environment involving different stakeholders and subject matter experts
• Enjoyment in working with a diverse group of people with different expertise

Nice to haves:

• Experience with or interest in ML, which powers many of the products we work on
• Experience in payments and/or fraud