Lead Fraud ML Engineer — Protect Billions of Transactions

Ramp; New York (NY); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Flexible PTO

Health and wellness stipend

Budget for intra-office travel

Weekly coffee stipend

Job summary

Ramp is seeking a lead for fraud machine learning to build core models, design data architectures, and set strategic roadmaps to mitigate fraud while minimizing friction for legitimate users.

You will partner with product and engineering on model design, implementation, execution and analysis, deploying production ML, and influencing processes and tools for scalable decisions in a fast‑paced startup environment.

Qualifications

• Bachelor’s degree in math, economics, physics, CS, or related field.
• 5+ years of industry experience as ML Engineer, Scientist, or Applied Scientist.
• Strong Python experience with numpy/pandas/sklearn/pytorch.
• Experience deploying ML models to production and backend systems.
• Strong SQL knowledge (Snowflake, Postgres).
• Familiar with AI tools for software development and data analysis.

Responsibilities

• Lead the fraud ML program, building core models and data architectures.
• Set strategic roadmaps to reduce fraud while minimizing user friction.
• Collaborate with product and engineering on model design, implementation, and analysis.
• Advocate scalable processes, tools, and practices for the ML team.

Skills

Machine Learning

Python

SQL

Production ML

Problem solving

Education

Bachelor's degree in quantitative field

PhD in quantitative field (nice to have)

Tools

Snowflake

Postgres

PyTorch

NumPy

scikit-learn

Job description

Ramp is seeking a lead for fraud machine learning to build core models, design data architectures, and set strategic roadmaps to mitigate fraud while minimizing friction for legitimate users.

You will partner with product and engineering on model design, implementation, execution and analysis, deploying production ML, and influencing processes and tools for scalable decisions in a fast‑paced startup environment.