Software Engineering Intern (Summer 2027)

About the Role

Scale's internship program offers interns the opportunity to own real, shipped work on the same roadmaps as full-time engineers, with mentorship from world-class talent. The culture values ownership, speed, and truth-seeking.

Example Projects

• Build reinforcement learning and post-training data pipelines that power frontier model development
• Develop evaluation infrastructure that measures model reliability for enterprise and public sector customers
• Ship agentic AI applications and the tooling that makes them observable, testable, and safe to deploy
• Ship tools that accelerate the growth of new qualified contributors on Scale's platform
• Build fraud-detection systems that remove bad actors and keep Scale's contributor base safe and trusted
• Use models to estimate the quality of tasks and contributors, and guarantee quality on requests at large scale
• Devise advanced matching algorithms that pair contributors to customers for optimal turnaround and accuracy
• Create optimized and efficient UI/UX tooling, in combination with ML algorithms, for 100k+ contributors completing billions of complex tasks
• Develop new AI infrastructure products to visualize, query, and explore Scale data

Requirements

• A graduation date in Fall 2027 or Spring 2028 with a Bachelor's degree (or equivalent) in a relevant field (Computer Science, EECS, Computer Engineering, Statistics)
• Available for a Summer 2027 internship (May/June start dates) in San Francisco
• Product engineering experience such as building web apps full-stack, integrating with relevant APIs and services, talking to customers, figuring out 'what' to build and then iterating
• Previous Computer Science/Software Engineering internship experience
• Track record of shipping high-quality products and features
• Experience building systems that process large volumes of data
• Experience with Python, Typescript, React, and/or MongoDB

Nice to Have

• Hands-on experience with LLMs, evaluations, or agentic systems — whether from coursework, research, or personal projects
• Open source contributions or a portfolio of shipped side projects