7 hours ago
Doha, QatarIntern
Responsibilities
- Build and ship full-stack products and features on Scale’s engineering roadmaps.
- Develop reinforcement learning and post-training data pipelines for frontier model development.
- Build evaluation infrastructure for measuring model reliability.
- Ship agentic AI applications and tooling for observability, testing, and safe deployment.
- Develop fraud detection, contributor quality, matching, UI/UX, and AI infrastructure systems at scale.
- Iterate on products by integrating APIs and services and incorporating customer needs.
Requirements
- Graduation in Fall 2027 or Spring 2028 with a bachelor’s degree or equivalent in Computer Science, EECS, Computer Engineering, Statistics, or a related field.
- Availability for a Summer 2027 internship with May or June start dates in San Francisco.
- Product engineering experience, including building full-stack web applications and integrating APIs or services.
- Previous computer science or software engineering internship experience.
- A 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.
- Hands-on experience with LLMs, evaluations, or agentic systems is preferred.
- Open-source contributions or a portfolio of shipped side projects is preferred.
Benefits
- Summer 2027 internship with May or June start dates.
- Interns receive mentorship from experienced engineers and work on real production roadmaps.
- The stated work location is San Francisco.
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.