Machine Learning Engineer

New York, NY (HQ) · Remote (US) · 5+ years experience · Full-time · Posted Sep 23, 2026

At a glance

Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ 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 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.

The Machine Learning Engineer will lead the future of fraud machine learning at Ramp.
This role involves building core machine learning models, designing data architectures, and setting strategic roadmaps to mitigate fraud while minimizing user friction.
The engineer will partner with product and engineering teams across model design, implementation, execution, and analysis.

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.
Partner with Fraud Engineering and Data Platform teams to augment and leverage data across first and third party sources.
Contribute to the culture of the machine learning team by influencing processes, tools, and systems for scalable decision-making.

Bachelor’s degree or above in Math, Economics, Physics, Computer Science, or other quantitative fields.
A minimum of 5 years of industry experience as a Machine Learning Engineer, Applied Scientist or Data Scientist.
Strong Python experience (numpy, pandas, sklearn, pytorch etc.) across ML techniques and backend engineering.
Prior experience deploying Machine Learning models to production and making meaningful contribution to backend systems.
Strong knowledge of SQL (Snowflake, Postgres, etc.).
Fluency with agentic (AI) tools for software development and data analysis.
Ability to thrive in a fast-paced, start-up environment.