Stripe

Machine Learning Engineer, Credit Intelligence


San Francisco, Remote (North …



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Job's description


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

Credit Intelligence brings together machine learning with product development to lower Stripe’s credit risk at scale, while retaining a best in class user experience.  Getting this tradeoff right is critical to Stripe’s long term success and profitability. We protect Stripe’s brand while also protecting the company from credit losses that can put our financial position at risk.

The Credit Intelligence team consists of machine learning, backend, and full stack engineers who want to tackle this problem through creative new product ideas and impactful machine learning models. We are undertaking several new efforts, where you can have an outsized impact on the architecture, implementation, and design choices behind these systems. 

What you’ll do

  • Design and deploy new models to iteratively improve Stripe’s business-critical models and systems that understand a user’s credit risk
  • Build the next generation of model training and scoring infrastructure, in close collaboration with our infrastructure teams
  • Imagine new feature ideas and design data pipelines to incorporate them into our models
  • Improve the way we evaluate and monitor our model and system performance

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

  • An advanced degree in a quantitative field (e.g. computer science, stats, physics, engineering)
  • A solid experience in software engineering in a production environment
  • Experience designing and training machine learning models to solve critical business problems
  • Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis

Preferred qualifications

  • The ability to thrive in a collaborative environment involving different stakeholders and subject matter experts
  • Pride in working on projects to successful completion involving a wide variety of technologies and systems




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