Before Stripe, every growing internet platform had a payments team. Today, every growing internet platform has an Identity team. Identity verification is a core piece of economic infrastructure for online businesses. Great identity solutions can help platforms automate the process of satisfying regulatory obligations while keeping their users safe. Join Stripe to help build a service that empowers platforms to take the burden and cost out of identity verifications and scale globally with ease.

We’re looking for a Machine Learning engineer to help envision, build, and deploy novel approaches for detecting fraud and identity theft while maintaining a great user experience. This role is perfect for engineers interested in using big data to model complex relationships across hundreds of millions of signals to identity patterns of malintent.

You will:

• Design and deploy new models using tools such as XGBoost, Tensorflow, PyTorch and iteratively improve Identity verification models to protect millions of users from fraud
• Work with huge payment datasets to find creative new methods of detecting and deterring identity theft
• Imagine new feature ideas and design real-time data pipelines to incorporate them into our models
• Work with risk and policy teams to understand our risk programs and address their needs
• Improve the way we evaluate and monitor our model and system performance

We’re looking for someone who has:

• An advanced degree in a quantitative field (e.g. stats, physics, computer science) and some experience in software engineering in a production environment
• 5+ years industry experience doing software development on a data or machine learning team
• 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
• 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
• Nice to have: previous experience or interest in the fraud or risk space