Staff Machine Learning Engineer, Transaction Risk

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 

The Transaction Risk organization optimizes each of the billions of dollars of transactions processed by Stripe annually on behalf of our users, maximizing successful transactions while minimizing payment costs and fraud. We own products like Radar end-to-end, developing machine learning models, building fast and scalable services and creating intuitive user experiences. We serve real-time predictions as part of Stripe’s payment infrastructure and architect controls that leverage ML to optimally manage users’ business.

What you’ll do

As a Staff machine learning engineer, you will design and build ML models, platforms and services that are configurable and scalable around the globe. You will partner with many functions at Stripe, with the opportunity to both work on ML models and systems, as well as produce direct user-facing business impact.

Responsibilities

• Design, train and deploy new models using advances in deep learning to iteratively improve Stripe’s business-critical models and systems in identity verification workflow
• Analyze and model the lifecycle of consumers using Stripe to support offering a wide variety of financial services to them
• Think of creative new methods to deter transaction risk, payment fraud and identity theft, while working against constantly evolving adversaries
• Explore green-field projects and convert abstract requirements into concrete deliverables
• Design the next generation of model training and scoring infrastructure, in close collaboration with our ML infrastructure teams
• Improve the way we evaluate and monitor our model and system performance
• Collaborate with stakeholders and drive projects involving a wide variety of technologies and systems to successful completion
• Mentor and support other engineers in training and deploying new deep learning models

Who you are

We’re looking for ML engineers with a strong background and passion in building successful backend systems or/and service APIs that deliver impactful product values and ML qualities to our customers. You are comfortable in dealing with changes. You love to take initiatives, and bias towards action.

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. stats, physics, computer science) and experience deploying models in a production environment
• 7+ years industry experience working on machine learning applications
• 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

• Experience in the fraud or risk space