About the RoleWe'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
Benefits available to all full-time Ramp employees (Global)- Flexible PTO
- Centralized home-office equipment ordering
- Health and wellness stipend
- Budget for intra-office travel
- Weekly coffee stipend
United States- 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents
- One Medical annual membership
- 401(k), including employer match on contributions made while employed by Ramp
- Fertility HRA (up to $10,000 per year)
- Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay
- Pet insurance
- In-office perks: lunch, snacks, drinks, and more
- Relocation expense coverage to NYC or SF (if needed)
Canada- Group medical, dental, and vision coverage through Sun Life
- Life, AD&D, and disability coverage
- Fertility drug coverage (up to $4,000 lifetime)
- Group Retirement Plan with employer match (RRSP + DPSP)
- Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay
- Employee Assistance Program and virtual care through Lumino Health
United Kingdom- Private medical insurance through Freedom Elite
- Virtual GP and at-home care via eMed x Livi
- Workplace pension through Penfold, with salary sacrifice option
- Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay
Referral InstructionsIf you are being referred for the role, please contact that person to apply on your behalf.
Other noticesPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Beware of recruiting scams: Ramp will only contact you through official [redacted].com email addresses and will never ask for payment or sensitive personal information during the hiring process.
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