ML / AI Research Intern

About the role#

As an ML / AI Research Intern, you will own an end-to-end project focused on building an intelligent access engine. You will research, prototype, and validate a system that combines clustering algorithms with generative AI to surface access recommendations. This 12-week project is designed to run in parallel with current engineering priorities, allowing you to take a concept from initial research to a working prototype and final presentation.

What you'll do#

• Own the project lifecycle, including research, data pipeline development, prototype creation, and final presentation.
• Research, implement, and compare unsupervised learning approaches—such as k-means, hierarchical clustering, and graph-based methods—to identify peer-group cohorts from employee attributes and entitlement data.
• Integrate an LLM layer to translate ML outputs into human-readable cluster names and recommendation rationales for non-technical administrators.
• Design and generate realistic datasets, including employee attributes, entitlement histories, and usage signals, to support training, evaluation, and offline testing.
• Document your methodology, experiments, results, and recommendations in a formal report to inform the production roadmap.

What you'll need#

• A strong background as an undergraduate or recent graduate in Computer Science with foundational knowledge of AI, ML, and generative AI concepts.
• Practical experience using LLMs to build projects, whether through coursework, hackathons, or side projects.
• A solid understanding of supervised and unsupervised learning, feature engineering, and model evaluation.
• Exposure to experiment tracking, reproducibility, or structuring ML code for reuse.
• The ability to explain complex work clearly to both technical and non-technical audiences in writing and conversation.

Location & details#

• Location: Bellevue, Washington (On-site)
• Term: Summer 2026
• Commitment: Full-time paid internship