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 focus on seeing how LLMs can solve some of our hardest problems in merchant risk and understanding how we can align language to our immense ocean of payments data. Some of our latest innovations have been around understanding how to best represent payments using transformers and enabling entirely new product ideas that are only made possible by GenAI.

 

What you'll do

As a Machine Learning Engineer on the Foundation Model 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 Foundation Models that power our payments and risk solutions . 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.


• Fine-tune and optimize LLMs using SOTA techniques, including multimodal alignment, knowledge distillation, quantization, and reasoning to build highly specialized models.


• 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 experimentation, 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

• 5+ 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 Python with demonstrated ability to write production-quality code


• A strong experimental mindset, with a proven track record of identifying, prototyping, and validating SOTA machine learning methods to enhance model performance.


• 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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