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Ramp is building smart infrastructure for finance teams, automating how over $200B in annualized spend flows through 70,000+ companies. The company handles payment authorization, risk flagging, spend categorization, and financial close processes.
You will lead the future of fraud machine learning at Ramp, building core ML models, designing data architectures, and setting strategic roadmaps to mitigate fraud-related threats while minimizing friction for legitimate users. You'll partner closely with product and engineering teams across model design, implementation, execution, and analysis.
Key responsibilities:
- Apply statistical and machine learning techniques to large datasets to discover patterns of fraud, platform abuse, and identity theft
- Prototype and productionize ML models and rules-based systems to protect Ramp and its users from fraud
- Partner with Fraud Engineering and Data Platform teams to augment and leverage data from first and third-party sources
- Contribute to the ML team culture by influencing processes, tools, and systems for scalable decision-making
Ramp values high agency, high urgency, and builders who own problems end-to-end. The median Ramp customer saves 5% and grows revenue 16% in their first year.
REQUIREMENTS:
- Bachelor's degree or above in Math, Economics, Physics, Computer Science, or other quantitative fields
- Minimum 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 ML models to production and making meaningful contributions to backend systems
- Strong SQL knowledge (Snowflake, Postgres, etc.)
- Fluency with agentic (AI) tools for software development and data analysis
- Ability to thrive in a fast-paced, constantly improving startup environment focused on 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 modern data stack (Snowflake, Hex, dbt, RisingWave, etc.)
- Strong perspective on data science + ML engineering development cycle in a post-AI setting
- Experience developing LLM-backed systems or tools
