7 hours ago
Doha, QatarEntry Level
Responsibilities
- Build and ship customer-facing AI applications and product features.
- Develop reinforcement-learning and post-training data pipelines for frontier model development.
- Create evaluation infrastructure for measuring model reliability.
- Build agentic AI applications and tooling that is observable, testable, and safe to deploy.
- Develop fraud-detection, contributor-quality, matching, and contributor-growth systems at scale.
- Build efficient UI/UX tooling, AI infrastructure products, and customer-service RAG applications.
- Integrate machine-learning models into customer systems and iterate directly with customers.
Requirements
- Graduate in Fall 2026 or Spring 2027 with a bachelor’s degree or equivalent in a relevant field such as Computer Science, EECS, Computer Engineering, or Statistics.
- Have product engineering experience, including building full-stack web applications, integrating APIs and services, engaging with customers, and iterating on what to build.
- Have previous Product or Software Engineering internship experience.
- Demonstrate a track record of shipping high-quality products and features at scale.
- Have experience building systems that process large volumes of data.
- Have 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
- For candidates applying in Qatar, Scale will work with successful candidates to support the visa application process, subject to Qatari authority approval and required documentation.
- The company provides reasonable accommodations and is committed to an inclusive, equal-opportunity workplace.
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.