Level

Ramp

Applied Scientist Intern

AI in this role

Build and deploy machine learning models and LLM-driven solutions for financial risk, fraud, and credit as an Applied Science intern.

pytorchscikit-learnpythonllms
machine-learningdeep-learningfeature-engineeringstatistical-modeling
About Ramp

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 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.

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.

 

Ramp Applicant Privacy Notice

How we rate this

Applied Scientist Intern at Ramp rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Machine LearningDeep LearningFeature EngineeringStatistical ModelingPyTorchscikit-learnPythonLLMs

Questions you could be asked

  1. Tell me about a project where machine learning was part of your work. What did you do?
  2. Tell me about a project where deep learning was part of your work. What did you do?
  3. Tell me about a project where feature engineering was part of your work. What did you do?
  4. Tell me about a project where statistical modeling was part of your work. What did you do?
  5. Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Machine Learning, Deep Learning, Feature Engineering, Statistical Modeling, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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