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
Relevant fieldsComputer Science
