2 months ago
Responsibilities
- Build and ship production software on the same roadmaps as full-time engineers.
- Develop reinforcement-learning and post-training data pipelines for frontier model development.
- Build model evaluation infrastructure and agentic AI applications with observable, testable, and safe deployment tooling.
- Create fraud-detection systems, contributor-quality estimation systems, matching algorithms, and contributor-facing UI/UX tooling.
- Develop AI infrastructure products for visualizing, querying, and exploring Scale data.
- Work with mentors and iterate on products based on customer needs.
Requirements
- Graduate in Fall 2027 or Spring 2028 with a bachelor's degree or equivalent in Computer Science, EECS, Computer Engineering, Statistics, or a relevant field.
- Be available for a Summer 2027 internship beginning in May or June in San Francisco.
- Have product engineering experience building full-stack web applications, integrating APIs and services, engaging with customers, and iterating on product direction.
- Have previous Computer Science or Software Engineering internship experience.
- Demonstrate a track record of shipping high-quality products and features.
- Have experience building systems that process large volumes of data.
- Have experience with Python, TypeScript, React, and/or MongoDB.
- Preferred: hands-on experience with LLMs, evaluations, or agentic systems through coursework, research, or personal projects.
- Preferred: open-source contributions or a portfolio of shipped side projects.
Benefits
- Summer 2027 internship with May or June start dates.
- In-person location in San Francisco.
- Mentorship from experienced engineers and ownership of real shipped work.
- Potential opportunity to return to Scale as a full-time employee.
Tech Stack
Categories
About Scale AI
Scale AI builds data annotation services and AI development tools for enterprises and government agencies, sold as a platform and managed services. Its products include the Scale Generative AI Platform for building and evaluating agents and the Data Engine for collecting, curating, and labeling training data, including RLHF and model evaluation. Founded in 2016 and headquartered in San Francisco, the company is privately held and works across domains from computer vision to LLM applications.