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.
Machine learning is an integral part of almost every service at Stripe. It is a key investment area with products and use cases that span merchant and transaction risk, payments optimization, identity, and merchant data analytics and insights (Sigma). We are also using the latest generative AI technologies (such as LLMs and FMs) to re-imagine product experiences and developing AI Assistants both for our customers (e.g. Radar Assistant and Sigma Assistant), and also to make Stripes more productive across Support, Marketing, Sales, and Engineering roles within the company.
From a data perspective, 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. We use a combination of highly scalable and explainable models such as linear/logistic regression and random forests, along with the latest deep neural networks from transformers to LLMs. Some of our latest innovations have been around figuring out how best to bring transformers and LLMs to improve existing models and also enable entirely new product ideas that are only made possible by GenAI.
As part of the ML Foundations organization you will play a critical role in the acceleration of our Machine Learning journey at Stripe. As a technical leader you will create and drive the long term technical vision for ML Infrastructure. The Machine Learning Infrastructure organization provides infrastructure and support to run machine learning workflows and ship to production, tooling and operational capacity to accelerate the use of these workflows, and opinionated technical guidance to guide our users onto successful paths. You’ll work with engineering leadership and a large cross functional team including engineering, data scientists, product managers and platform infrastructure teams to build the powerful, flexible, and user-friendly systems that substantially increase ML-Ops velocity across the company.
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.
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