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

The Payment Fraud organization optimizes each of the billions of dollars of transactions processed by Stripe annually on behalf of our users, maximizing successful transactions while minimizing payment costs and fraud. We own products like Radar end-to-end, developing machine learning models, building fast and scalable services and creating intuitive user experiences. We serve real-time predictions as part of Stripe’s payment infrastructure and architect controls that leverage ML to optimally manage users’ business.

What you’ll do

As a machine learning engineer, you will design and build platforms and services that are configurable and scalable around the globe. You will partner with many functions at Stripe, with the opportunity to both work on infrastructure/platform systems, as well as produce direct user-facing business impact.

Responsibilities

• Design machine learning systems and pipelines for training and running machine learning models that improve the efficiency of transactions on Stripe. This could involve:
• Improving the accuracy of our prediction models for transaction outcomes, like whether a payment will be accepted or declined by the card network, or disputed as fraudulent by a cardholder.
• Understanding our users’ business needs in order to evaluate model performance and improve the value model we use to evaluate transaction outcomes.
• Developing and evaluating new model architectures which improve the accuracy of our prediction models.
• Incorporate new features and sources of data.
• Building prediction models for new aspects of transaction outcomes, like whether we expect to win a dispute given auto-submitted evidence.
• Writing simulation code on our distributed clusters to help us understand what would happen across different segments if we changed how we action our models.
• Integrating new models and behaviors into Stripe’s core payment flow.
• Collaborating with our machine learning infrastructure team to build support for new model types into our scoring infrastructure.
• Mentor engineers earlier in their technical careers to help them grow

Who you are

We’re looking for ML engineers with a strong background and passion in building successful backend systems or/and service APIs that deliver impactful product values and ML qualities to our customers. You are comfortable in dealing with changes. You love to take initiatives, and bias towards action.

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

• At least 5 years years industry experience doing end to end ML development on a data or machine learning team
• An advanced degree in a quantitative field (e.g. stats, physics, computer science) and some experience in software engineering in a production environment.
• Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis.
• The ability to thrive in a collaborative environment involving different stakeholders and subject matter experts.
• Pride in working on projects to successful completion involving a wide variety of technologies and systems.
• Comfort working directly with your users.
• Empathy with users and a strong customer focus
• Enjoyment in working with a diverse group of people with different expertise

Preferred qualifications

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