Software Engineer Intern – AI Applications

Cambium Assessment develops educational technology solutions that support individualized and differentiated instruction. The Software Engineer Intern will help build AI-powered educational assessment systems by developing LLM features, supporting AI agent workflows, integrating generative AI into learning platforms, and evaluating AI systems for quality, safety, and performance.

What you'll do:

• Assisting in the development of AI‑powered features using Large Language Models (LLMs)
• Supporting AI agent workflows, including prompt design, tool integration, and basic orchestration logic
• Helping integrate generative AI capabilities into educational platforms
• Contributing to evaluation and testing of AI systems for quality, safety, and performance
• Collaborating with engineers, product managers, and researchers on real‑world learning use cases
• Exploring responsible AI practices in a mission‑driven education environment

What they're looking for:

• You must reside and work in the US for this internship position
• Currently pursuing a postgraduate degree in Computer Science, Data Science, Software Engineering, AI/ML, or a related field
• Experience (academic or project‑based) with at least one programming language such as Python, JavaScript/TypeScript, or C#
• Familiarity with web development, APIs, or backend systems
• Strong problem‑solving skills and eagerness to learn
• Ability to collaborate effectively in a team environment
• Coursework or projects involving advanced AI, machine learning, or NLP
• Exposure to cloud platforms (AWS, Azure, or GCP)
• Interest in Generative AI, LLMs, or intelligent systems
• Interest in education technology or applied learning sciences

Benefits:

• Remote work arrangement for employees residing and working in the US
• Hands-on experience contributing to real AI products
• Learning from experienced engineers at the forefront of EdTech
• Practical experience working with modern AI and generative technologies
• Experience with how production-grade AI systems are designed, tested, and deployed
• Learning best practices for building safe, scalable, and user-focused AI features