Staff Software Engineer, Payments Intelligence
StripeLocation: New York, TorontoOpen for: 13 days
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StripeLocation: New York, TorontoOpen for: 13 days
StripeLocation: Toronto, CAOpen for: 18+ days
StripeLocation: Toronto, Vancouver, Canada-RemoteOpen for: 18+ days
StripeLocation: TorontoOpen for: 18+ days
StripeLocation: TorontoOpen for: 18+ days
StripeLocation: TorontoOpen for: 18+ days
StripeLocation: TorontoOpen for: 18+ days
StripeLocation: TorontoOpen for: 18+ days
StripeLocation: CanadaOpen for: 7 days
Open 13 days
It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.
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Last checked Nov 3, 2026.
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Who 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
Payment Intelligence is comprised of multiple product teams building AI-first solutions to some of our customers’ biggest challenges, including fraud/abuse ( Radar ), payment optimizations ( AuthBoost ), Disputes, Authentication, and merchant analytics. We’re a mix of machine engineering engineers and full-stack software engineers impacting ~every Stripe transaction, creating revenue opportunities for Stripe and our customers, and helping protect the broader ecosystem.
We have the benefit and privilege of working on cutting edge technologies with incredible scale and reach while also working directly with our merchants every day to build the right products for their needs.
What you'll do
As a Staff Engineer on Payment Intelligence, you’ll work across our product portfolio to ensure we’re building in a consistent, efficient, and effective manner. Experience with large, distributed systems on the critical path will help candidates succeed, as will comfort working across the stack and interacting with ML teams and models.
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