Ramp is recruiting a Machine Learning Engineer to join the team in New York. The position is full-time and hybrid, with some days on site and some from home. Pay for this role is set at $200,000 to $330,000 a year, according to the posting.
The description below comes from Ramp's own job posting.
Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ 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 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.
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, 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.
Ramp advertises $200,000 to $330,000 a year for this role.
It is hybrid. You would work partly from home and partly from the office in New York.
The posting mentions at least 5 years of relevant experience, a phd or doctorate or equivalent and skills such as Python, SQL, dbt and Snowflake. Read the full description above for the complete list.
Use the apply button on this page. It takes you to Ramp's own application form, so your CV goes directly to their hiring team.
Ramp published the role on September 23, 2026. We check the employer's job board every day and take this page down when the role closes, so if you can read it, the job was still listed at our last check.
Source: Ramp careers page (jobs.ashbyhq.com). Applications go straight to the employer. We check the listing daily and remove it once it closes.
Ramp publishes its open roles on its own careers board. This page collects the Ramp jobs that are live right now. Each listing links to Ramp's application form, and we remove a listing once the employer closes it.
Industry: Finance & Banking
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