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

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.

What you’ll do

As part of the ML Foundations organization you will play a critical role in the acceleration of our Machine Learning journey at Stripe. The organization develops the AI/ML foundational platform features and GenAI models to enable all Stripes to create AI/ML powered product features and applications. As a lead you will be responsible for helping build out the Machine Learning roadmap for the organization, end to end model development, and driving Machine Learning and Gen AI initiatives. You will also coach and mentor our ML engineering talent, and work closely with engineering leadership and large cross-functional teams including engineering, data scientists and product teams to help scale the AI/ML efforts.

Responsibilities

• Develop and execute against both short and long-term roadmaps. Make effective tradeoffs that consider business priorities, user experience, and a sustainable technical foundation.
• Design, implement, and scale critical machine learning model development to support company wide strategic initiatives.
• Improve existing models to help enable new product ideas and improve productivity for our users and across the company.
• Assist with team growth and development while maintaining a high bar for excellence and technical curiosity.
• Own and build cross-functional partnerships with stakeholders including dependency engineering teams, product, design, infrastructure, and operations.

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

• • Minimum of 15+ years of ML engineering industry experience OR equivalent combined work experience reflecting domain expertise as relevant to this position.
  • PhD in a relevant field (computer science, machine learning, AI, statistics, physics, …)
  • Strong understanding of machine learning approaches and algorithms: Deep Learning, LLM, Generative Models, and NLP.
  • Demonstrated experience of leading company-wide initiatives spanning multiple teams and organizations OR leveraging deep domain expertise to influence tech roadmap planning and execution.
  • Demonstrated ability to effectively collaborate across multiple teams and stakeholders to drive business outcomes.
  • Experience, mentoring, and investing in the development of ML engineers and peers.