Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
New York, NY (HQ) · Remote (US) · New York City, NY, USA
Open 5 months, first seen by us 155 days agoLocation type: HybridFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: DataFrom the employer’s posting
We’re seeking someone to lead the future of fraud machine learning at Ramp.
Find fraud, platform abuse, and identity theft patterns in large datasets using statistics and ML
Prototype and ship ML models and rules-based systems against fraud
Combine first and third party data to add context to each decision
Shape ML team processes, tools, and systems for scalable decisions
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.
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
influencing processes, tools, and systems that will allow us to make better decisions in a scalable way
Bachelor’s degree or above in Math, Economics, Physics, Computer Science, or other quantitative fields
Experience at a high-growth startup
Strong python experience (numpy, pandas, sklearn, pytorch etc.) across ML techniques and backend engineering
Experience with the modern data stack ( Snowflake / Hex / dbt / RisingWave / etc )
Prior experience deploying Machine Learning models to production and making meaningful contribution to backend systems
Experience developing LLM-backed systems or tools
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
Nice-to-Haves - PhD in Math, Economics, Physics, Computer Science, or other quantitative fields - Context on Fraud and/or Identity Threat detection systems
Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends.
In the employer’s words · Read in context · Company website ↗
Open 5 monthsWe first saw this posting 155 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Day 155
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,331 similar postings that came down. Coming down is not always a hire.
4 open jobs
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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
- 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
- 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
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
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 7.5% 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.
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.
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
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
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
Flexible PTO
Centralized home-office equipment ordering
Health and wellness stipend
Budget for intra-office travel
Weekly coffee stipend
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)
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
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
If you are being referred for the role, please contact that person to apply on your behalf.
Pursuant 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 @Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.
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New York City, NY, USA
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