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 RoleThe Applied Science team builds models and tools that solve Ramp’s most critical problems: from underwriting businesses to combatting fraud to making spend management smarter. We’re deeply embedded in the business and provide a quantitative foundation for decision making.
As an Applied Science intern, you’ll be a fully integrated member of the team and own your project from start to finish. Working with engineers, product managers, and business stakeholders, you’ll translate complex business needs into scalable machine-learning-driven solutions. This is a chance to apply ML concretely, ship code, and create genuine value for Ramp and our customers.
You will focus on exciting problems in areas like: credit, fraud, growth, or our core product.
End-to-End ML: own the model lifecycle from data exploration and feature engineering to training, benchmarking, deployment, and monitoring
State-of-the-Art AI: leverage the latest Large Language Models (LLMs) to solve novel problems and create new product capabilities for our customers
Versatile Techniques: apply the right tools to the right problems, whether it’s deep learning, gradient boosting, or causal inference
Rigorous Experimentation: quantify the impact of your work through A/B tests and other statistical methods
Collaborate: partner closely with product and business leaders to translate models and insights into actionable strategy and user-facing features
B.S., M.S. or Ph.D. Student: currently pursuing a degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or other quantitative fields with an expected graduation date between Dec 2027 - 2029. Graduate degrees are preferred, but not a must.
Strong ML Fundamentals: solid understanding of the mathematical foundations of machine learning, statistics, probability, and optimization
Strong Interest or Experience with AI: curiosity and drive to integrate cutting edge LLMs and agents into applied solutions
Python Proficiency: good grasp of common Data Science libraries (pandas, scikit-learn, NumPy, PyTorch, etc.)
SQL Knowledge: experience wrangling data in a modern data warehouse (e.g. Snowflake, BigQuery, Redshift, Clickhouse)
Practical Experience: track record of curating datasets and building/evaluating ML models
Strong Communication: ability to clearly explain complex concepts to both technical and non-technical audiences and use data to build a compelling narrative
Bias For Action: a comfort with ambiguity and desire to ship solutions quickly then iterate
Publications, Projects, or Previous Experience: relevant experience applying AI/ML and demonstrating your passion for the field
Production ML Mindset: knowledge of software engineering best practices applied to ML including version control (Git), testing, and writing maintainable code
Data Orchestration: experience with leveraging modern data orchestration platforms (Airflow, Dagster, Prefect, Metaflow)
The monthly rate for this internship is $12,500 USD + housing stipend
Apple MacBook
Catered lunches in NYC office Monday-Friday
Weekly coffee stipend
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 Applicant Privacy Notice
Skills Required
- Currently pursuing a B.S., M.S., or Ph.D. in Data Science, Computer Science, Mathematics, Physics, Economics, Statistics, or another quantitative field, with expected graduation between December 2027 and 2029
- Strong understanding of machine learning mathematics, statistics, probability, and optimization
- Strong interest in or experience with AI, LLMs, and agents
- Proficiency in Python and common data science libraries, including pandas, scikit-learn, NumPy, and PyTorch
- Knowledge of SQL and experience working with data warehouses such as Snowflake, BigQuery, Redshift, or ClickHouse
- Practical experience curating datasets and building and evaluating machine learning models
- Ability to communicate complex concepts to technical and non-technical audiences
- Comfort with ambiguity, bias for action, and willingness to ship and iterate quickly
- Relevant AI/ML publications, projects, or previous experience
- Knowledge of software engineering practices for production machine learning, including Git, testing, and maintainable code
- Experience with Airflow, Dagster, Prefect, or Metaflow
Ramp Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Ramp and has not been reviewed or approved by Ramp.
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Fair & Transparent Compensation — Fair & Transparent Compensation: Pay is positioned as competitive or top-of-market in core technical roles, with strong base pay and total compensation ranges cited for engineers and product roles. Compensation is also framed as including meaningful equity alongside salary, making offers feel compelling versus many startup benchmarks.
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Healthcare Strength — Healthcare Strength: Healthcare coverage is described as comprehensive, often including medical, dental, and vision, with additional primary-care access via a One Medical membership. The package is portrayed as above-average on employer coverage for employees, increasing perceived value of the benefits bundle.
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Retirement Support — Retirement Support: A 401(k) with an employer match is consistently included as a core benefit. Immediate or meaningful matching is presented as a concrete financial benefit that goes beyond a basic plan offering.
Ramp Insights
What We Do
Ramp is building the next generation of finance tools—from corporate cards and expense management, to bill payments and accounting integrations—designed to save businesses time and money with every click. More than 10,000 customers cut their expenses by 3.5% per year and closing their books 8x faster by switching to the Ramp platform. Founded in 2019, Ramp powers the fastest-growing corporate card and bill payment software in America and enables billions of dollars of purchases each year. Ramp continues to grow at an increasingly large scale, more than doubling its revenue run rate in the first half of 2022. Valued at $8.1 billion, Ramp's investors include Founders Fund, Stripe, Citi, Goldman Sachs, Coatue Management, D1 Capital Partners, Redpoint Ventures, General Catalyst, and Thrive Capital, as well as over 100 angel investors who were founders or executives of leading companies. The Ramp team comprises talented leaders from leading financial services and fintech companies—Stripe, Affirm, Goldman Sachs, American Express, Mastercard, Visa, Capital One—as well as technology companies such as Meta, Uber, Netflix, Twitter, Dropbox, and Instacart. Ramp was named Fast Company’s most innovative finance company in 2022.
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