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.

Machine Learning at Stripe

Machine learning is an integral part of almost every service at Stripe. Key products and use-cases powered by ML at Stripe include merchant and transaction risk, payments optimization and personalization, identity verification, and merchant data analytics and insights. We are also using the latest generative AI technologies to re-imagine product experiences, and are developing AI Assistants both for our customers and to make Stripes more productive across Support, Marketing, Sales, and Engineering roles within the company.

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 enable entirely new product ideas that are only made possible by GenAI. Stripe’s ML models serve millions of users daily and reduce financial risk, increase payment success rate, and grow the GDP of the internet. We work on challenging problems with large business impact, and seek to foster creativity and innovation.

About the team

Stripe Capital provides access to fast, flexible financing to small-and-medium businesses on Stripe to accelerate their growth, and we lent over $1B in 2024. Businesses use the funds for marketing, team growth, geographic expansion, working capital, new equipment purchases, and much more.

Machine learning is core to Stripe Capital’s business—we use information about businesses from their activity within and outside of Stripe and our models to automatically underwrite uniquely tailored financing offers to their needs, which banks are often unable to do. We are doing so through models with an established performance history, data infrastructure that is Stripe scale, and a strong feedback loop that includes anomaly detection and a risk portfolio management layer. We're an end-to-end team going from ideas to models to shipping in production.

What you’ll do

As a machine learning engineer for Stripe Capital, you will be responsible for designing, building, training, evaluating, deploying, and owning ML models in production with the goals of providing financing opportunities to as many users as possible while satisfying financial performance goals. You will work closely with software engineers, data scientists, product managers, and risk managers to operate Stripe’s ML powered systems, features, and products. You will also contribute to and influence ML architecture at Stripe and be a part of a larger ML community.

Responsibilities

• Design state-of-the-art ML models and large scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints
  
• Experiment and iterate on ML models (using tools such as PyTorch and TensorFlow) to achieve key business goals and drive efficiency
  
• Develop pipelines and automated processes to train and evaluate models in offline and online environments
  
• Integrate ML models into production systems and ensure their scalability and reliability
  
• Collaborate with product and strategy partners to propose, prioritize, and implement new product features
  
• Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions
  

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

• 3+ years of industry experience building and shipping ML systems in production
  
• Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark
  
• Knowledge of various ML algorithms and model architectures
  
• Hands-on experience in designing, training, and evaluating machine learning models
  
• Hands-on experience in productionizing and deploying models at scale
  
• Hands-on experience in orchestrating complicated data pipelines and efficiently leveraging large-scale datasets
  
• Hands-on experience in collaborating across multiple teams, especially Data Science and Risk Management teams
  

Preferred qualifications

• MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)
  
• Experience with Deep Learning including the latest architectures such as transformers, test-time compute, reinforcement learning
  
• Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
  
• Experience in adversarial domains such as Lending, Trading, Fraud
  
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