Summer 2026 | Applied Scientist Intern

Ramp
New York, NY (HQ)Posted 11 September 2025

Tech Stack

Job Description

About RampAt Ramp, we’re rethinking how modern finance teams function in the age of AI. We believe AI isn’t just the next big wave. It’s the new foundation for how business gets done. We’re investing in that future — and in the people bold enough to build it.Ramp is a financial operations platform designed to save companies time and money. Our all-in-one solution combines payments, corporate cards, vendor management, procurement, travel booking, and automated bookkeeping with built-in intelligence to maximize the impact of every dollar and hour spent. But we’re not just building features powered by AI. We’re building a platform where agents can chase receipts, close books, flag risks, and surface insights. This enables teams to reclaim their time and reinvest in what matters.More than 40,000 businesses, from family-owned farms to space startups, have saved $10B and 27.5M hours with Ramp. Founded in 2019, Ramp powers the fastest-growing corporate card and bill payment platform in America, and enables over $80 billion in purchases each year.Ramp’s investors include Thrive Capital, Sands Capital, General Catalyst, Founders Fund, Khosla Ventures, Sequoia Capital, Greylock, and Redpoint, among others, in addition to 100+ angel investors who have been founders or executives of leading companies.Ramp has been named to Fast Company’s Most Innovative Companies list and LinkedIn’s Top U.S. Startups for more than 3 years, as well as the Forbes Cloud 100, CNBC Disruptor 50, and TIME Magazine’s 100 Most Influential Companies.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'll focus on exciting problems in areas like: credit, fraud, growth, or our core product! What You’ll DoEnd-to-End ML: own the model lifecycle from data exploration and feature engineering to training, benchmarking, deployment, and monitoringState-of-the-Art AI: leverage the latest Large Language Models (LLMs) to solve novel problems and create new product capabilities for our customersVersatile Techniques: apply the right tools to the right problems, whether it’s deep learning, gradient boosting, or causal inferenceRigorous Experimentation: quantify the impact of your work through A/B tests and other statistical methodsCollaborate: partner closely with product and business leaders to translate models and insights into actionable strategy and user-facing featuresWhat You NeedM.S. or Ph.D. Student: currently pursuing degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or other quantitative fields with an expected graduation date between Dec 2026 - 2027Strong ML Fundamentals: solid understanding of the mathematical foundations of machine learning, statistics, probability, and optimizationPython 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 modelsInterest or Experience with AI: curiosity and drive to integrate cutting edge LLMs and agents into applied solutionsStrong Communication: ability to clearly explain complex concepts to both technical and non-technical audiences and use data to build a compelling narrativeBias For Action: a comfort with ambiguity and desire to ship solutions qui ... (truncated, view full listing at source)
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