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Active1mo ago

Machine Learning Engineer

Ramp·📍 New York City, New York, United States

Employment

FULL TIME

Work mode

Remote

Experience

SENIOR LEVEL

Salary

$200K – $330K/yr

Pay vs market · Above median

This job's pay ($265,000 a year at the midpoint) is higher than about 70% of 270 salary-listed Machine Learning Engineer jobs in United States in the last 12 months; their median is $225,450.

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Role overview

Job description

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. 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 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 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 Instructions If you are being referred for the role, please contact that person to apply on your behalf. Other notices 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. Ramp Applicant Privacy Notice

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About this listing

  • Source: Ramp's official careers site · still listed there on 9 Nov 2026
  • Posted 23 Sept 2026

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Role market pulse

How Machine Learning Engineer demand looks in United States

72/100 · rising

Live jobs

362

Posted 30d

259

30d movement

+388.7%

Remote share

30.9%

Salary listed

85.6%

Salary trend 1Y

Not enough history

What they are looking for

Skills & requirements

Qualification

Other notices Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records

Department · Data

Hiring company

Ramp

Make expense management easy with Ramp's spend management platform. Combine global corporate cards, travel, expenses and accounts payable to automate finance operations and improve efficiency.

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