Job Description

Who we are

About Stripe

Stripe’s mission is to accelerate global economic and technological development. We offer financial infrastructure and a variety of services to serve the needs of a wide range of users, from startups to enterprises, with global scale and industry-leading reliability and product quality. All financial services businesses face a trade-off between access, which we want to expand, and risk, which we want to minimize. We use machine learning to scalably and intelligently optimize across both.

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. We are also using the latest generative AI technologies (such as LLMs and FMs) to re-imagine product experiences and developing AI Assistants and Agents 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.

About the team

We are dedicated to building and shipping the foundational AI and machine learning systems that will power our entire product suite. Our mission is to fundamentally transform how Stripe uses ML, leveraging our extensive and rich dataset to solve some of the most challenging problems in payments and fraud. We work closely with our partners in Risk, Payments, and Support to build transformative technologies that have a direct impact on our users.

From a data perspective, Stripe handles over $1.4T in payments volume per year, which is roughly 1.3% 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.

What you’ll do

As a Machine Learning Engineer on the ML Foundations team, you'll solve some of Stripe's most challenging technical problems that span multiple teams and directly impact our research and engineering efforts around building the Stripe Foundation Models, Assistants, and Agents. You'll be responsible for both hands-on technical contributions and driving strategic initiatives that shape how ML systems operate at scale across Stripe.

Responsibilities

• Develop foundation models for payments, merchants, and consumers that span Stripe product areas


• Develop Universal AI Assistants and AI Agents to answer questions and automate tasks across Stripe products


• Drive technical excellence through hands-on contributions to the design and development of state-of-the-art AI/ML systems, conducting architecture reviews, and maintaining high code quality


• Partner with engineering and product leaders across Stripe to identify and prioritize foundational investments such as foundation models, assistants, and agents that unlock new capabilities for product teams


• Contribute to Stripe's technical strategy by representing AI/ML engineering perspectives in company-wide technical decisions and roadmap planning


• Mentor ML engineers across Stripe on ML systems design, helping teams navigate complex technical trade-offs, and adopt platform capabilities effectively  

Who you are

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

• 10+ years of experience building and shipping ML models that power AI/ML product features, with a strong emphasis on modern technologies such as DNNs, Transformers, and Foundation Models


• Strong programming skills in languages used for ML systems (Python, Java, Scala, or Go) with demonstrated ability to write production-quality code


• A strong builder mindset, with the ability to define a team's charter and lead the development of complex systems from scratch


• Proven ability to shepherd large, complex ML projects and drive transformational change in an organization


• Deep passion for solving really interesting problems and for building the latest technologies rather than relying on outdated methods

Preferred qualifications

• A PhD or Master's degree with a research-oriented background, with the ability to dive into research papers and stay current with academic publications 


• Experience with a large-scale, data-rich product in a domain such as payments, commerce, search, or social media


• Knowledge of the challenges and opportunities in applying ML to fraud prevention, merchant intelligence, or financial services


• Published research or open source contributions in AI/ML or related fields 

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