Machine Learning Engineer

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ABOUT RAMP

Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.

The problems are high-stakes, data-dense, and unforgiving.

We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.

The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.

If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.

ABOUT THE ROLE

We’re seeking someone to lead the future of fraud machine learning at Ramp. In this role, you will help build core machine learning models, design data architectures, and set strategic roadmaps to help Ramp mitigate fraud-related threats while minimizing the friction experience by legitimate users. You will partner closely with product and engineering counterparts across model design, implementation, execution, and analysis.

WHAT YOU’LL DO

• Employ statistical and machine learning techniques on large datasets to discover patterns of fraud, platform abuse, and identity theft

• Prototype and productionize machine learning models and rules-based systems to protect Ramp and its users from fraud

• Partner closely with Fraud Engineering and Data Platform teams to augment and leverage data across first and third party sources, ensuring we’ve added as much context as possible to every decision we make