Applied Scientist Intern

Ramp; United States; Remote; USD 28,000 - 55,000; Part time

Job summary

Ramp’s Applied Science team seeks an intern to own a project from start to finish, collaborating with engineers, product managers, and business stakeholders to translate complex business needs into ML-driven solutions.

This role emphasizes end-to-end ML work—from data exploration and feature engineering to training, benchmarking, deployment, and monitoring—while exploring LLMs and practical AI applications that create real value for Ramp and its customers.

Qualifications

• Strong ML foundations in mathematics, statistics, probability, and optimization.
• Interest or experience with AI, including LLMs and agents, to build applied solutions.
• Pursuing a degree in a quantitative field with graduation between Dec 2027 and 2029.

Responsibilities

• End-to-End ML: manage the model lifecycle from data exploration to training, benchmarking, deployment, and monitoring.
• State-of-the-Art AI: leverage LLMs to solve novel problems and create product capabilities.
• Versatile Techniques: apply deep learning, gradient boosting, or causal inference as appropriate.
• Rigorous Experimentation: quantify impact with A/B tests and statistical methods.
• Collaborate with product and business leaders to translate models into actionable features.

Skills

ML Fundamentals

AI interest

Strong curiosity

Education

Pursuing quant degree (e.g., Data Science, CS, Math)

Tools

Python

pandas

scikit-learn

Job description

About RampRamp

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 Role

The 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.

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