Staff ML Engineer, Supportability Intelligence

Stripe builds the economic infrastructure for the internet. To accomplish this ambitious agenda, Stripe needs to enable business, banks, payment infrastructure and customers at scale. Rapid scaling exposes Stripe to risks of various forms like fraud, ability to certify merchants (merchant supportability), loss risk due to credit chargebacks. Building trust between banks, businesses & customers is a key ingredient for Stripe to be successful and we are building best-in-class ML/AI to help deal with risk mitigation at scale and developing high partner trust.

The Supportability Intelligence team is responsible for building ML models to predict merchants that represent a supportability risk to Stripe and its partners.

Our work impacts Stripe in multiple ways including, but not limited to, Business Enablement, Partner Compliance and Risk Management. In short we impact Stripe's bottom line and we help build a safer financial backbone for the internet.

Our ambition is to build industry-leading solutions to hard problems like:

• Modeling reputational risk
• Building representations (embeddings) from merchant profiles, categories of products (that the merchant sells), transaction profile etc.
• Using such embeddings to build downstream models for supportability, credit and fraud risk
• Building risk engineering systems at scale, to help Stripe grow its footprint rapidly

You will have an outsized impact on the direction, design & implementation of the solutions to these problems.

Your work will include:

• Setting the technical & process direction for the team based on business goals
• Building innovative AI based solutions to implement product ideas that directly increase Stripe's ability to support merchants
• Building and maintaining sophisticated ML models to understand merchants and detect bad actors
• Building systems that evaluate businesses for risk and take appropriate actions
• Helping our partner teams design and launch new policies that directly impact Stripe's bottom line
• Helping engineers across the company to develop technologies consume our models, score and artifacts
• Debugging production issues across services and multiple levels of the stack

You may be a good fit if you:

• Have at least 7 years of software engineering or Machine Learning experience
• Have led multiple engineers to deliver large-scale high-impact projects
• Enjoy and have experience building scalable ML backend infrastructure
• Hold yourself and others to a high bar when working with production systems
• Thrive in a collaborative environment
• Have experience with building and deploying ML models, especially using NLP on Tensorflow or pytorch
• [Bonus] Have experience in Python, Scala (Spark), or Ruby
• [Bonus] Have experience in application of deep learning to risk problem areas including transformer based models for multi-class/multi-label predictions, fine-tuning of pre-trained NLP models, few-shot learning for adapting to constantly changing risk environments etc.