At a glance
Build and ship machine-learning solutions across credit, fraud, growth, and spend management—from feature engineering through deployment and monitoring—while applying LLMs and rigorous experimentation to real customer problems.
Summarized by AI from the original posting
What you'll do
- Own the model lifecycle from data exploration and feature engineering through training, benchmarking, deployment, and monitoring
- Use LLMs to solve novel problems and create product capabilities
- Apply deep learning, gradient boosting, or causal inference
- Measure impact through A/B tests and statistical methods
- Collaborate with product and business leaders to translate models into strategy and user-facing features
Requirements
- 01Strong machine-learning fundamentals
- 02Strong interest or experience with AI, LLMs, and agents
- 03Python proficiency
- 04SQL knowledge
- 05Practical ML modeling experience
- 06Strong communication skills
- 07Comfort with ambiguity and bias for action
Perks
Skills
Full description
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.
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.
What You’ll Do
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
What You Need
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
Nice to Haves
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)
Compensation
The monthly rate for this internship is $12,500 USD + housing stipend
Ramp Benefits
Apple MacBook
Catered lunches in NYC office Monday-Friday
Weekly coffee stipend
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.