Machine Learning Engineer, Identity Product

Who we are : Stripe is a financial infrastructure platform for businesses. Millions of companies use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet.

Machine Learning at Stripe

Machine learning is an integral part of almost every service at Stripe. Key products and use-cases powered by ML at Stripe include merchant and transaction risk, payments optimization and personalization, identity verification, and merchant data analytics and insights.

Stripe handles over $1T in payments volume per year, which is roughly 1% of the world’s GDP. We process petabytes of financial data using our ML platform to build features, train models, and deploy them to production.

About the team

Before Stripe, every growing internet platform had a payments team. Today, every growing internet platform has an Identity team to verify trustworthiness of a user. Identity verification is a core piece of economic infrastructure for online businesses that enables businesses to digitally verify the user’s Identity for fraud, regulatory and trust & safety purposes.

Why Stripe Identity?

• Be part of a team that's building a product with the potential to transform how trust is established in the digital world
• Work on challenging technical problems at a global scale
• Contribute to a product that will serve diverse industries, from fintech to e-commerce and beyond
• Opportunity to shape the future of digital identity and fraud prevention

What you’ll do

We are looking for Machine Learning Engineers to own the end-to-end lifecycle of applied ML model development and deployment in service of consumer facing product Identity. You will work closely with software engineers, machine learning engineers, data scientists, and ML platform infrastructure teams to design, build, deploy, and operate Stripe’s ML-powered decisioning systems.

Responsibilities

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role.

Minimum requirements

Preferred qualifications

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