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About the Role

Join Replit's key teams across the company, such as AI Research, Strategic Finance, or the Office of the CEO, for a unique paid internship built for sharp quantitative and creative minds. You will work alongside our top executives, in addition to world-class engineers, designers, and finance team on some of the hardest problems in AI-native software creation and accelerating key areas of our business.

We are creating a dedicated track for students with strong mathematical backgrounds because the problems we are solving sit at the intersection of deep math and applied AI, including agent reasoning, systems optimization, and improving how our models learn and perform at scale. Your work will directly shape how millions of users build software.

You Will:

- Contribute to real engineering problems that push the boundaries of AI-powered software creation

- Collaborate with engineers, designers, and product managers on infrastructure that powers Replit's platform

- Prototype novel approaches to problems in AI, systems, or tooling where mathematical rigor is the differentiator

- Ship work that impacts millions of developers globally, in an environment where your ideas are heard and often implemented

Required Skills and Experience:

- Currently pursuing a Bachelor's, Master's, or PhD in Mathematics, Computer Science, Computer Engineering, Statistics, Physics, or a related quantitative field

- At least one semester of schooling remaining after the internship

- Demonstrated excellence in competitive mathematics such as IMO and IOI, quantitative research, or advanced coursework

- Genuine curiosity about AI, agent systems, company building, or developer tooling

- Extremely bullish on Replit and the future of AI-native software creation. We would love to see what you have built on the platform

What We Value:

- First-principles thinking: Ability to break down complex problems from the ground up and reason about them rigorously

- Mathematical intuition: Strong grasp of the underlying math behind machine learning, optimization, or systems

- Self-directed and autonomous: Capable of working independently while collaborating well with cross-functional teams

- Strong communication: Ability to explain complex quantitative concepts to both technical and non-technical audiences

- Curiosity and speed: Genuine excitement for hard problems and the drive to iterate quickly