Machine Learning Engineer

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.

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
• Contribute to the culture of Ramp’s machine learning team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way

What You Need

• 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, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions

Nice-to-Haves

• PhD in Math, Economics, Physics, Computer Science, or other quantitative fields
• Context on Fraud and/or Identity Threat detection systems
• Experience at a high-growth startup
• Experience with the modern data stack (Snowflake / Hex / dbt / RisingWave / etc)
• Strong perspective on data science + ML engineering development cycle, especially in a post-AI setting
• Experience developing LLM-backed systems or tools

Referral Instructions

If you are being referred for the role, please contact that person to apply on your behalf.