ML / AI Research Intern

Oleria Security•Bellevue, WA

About The Position

Oleria is building the next generation of intelligent Identity Governance and Administration (IGA). A key capability on our roadmap is using machine learning to automatically understand access patterns across the workforce and translate those insights into actionable, human-readable recommendations. As our ML / AI Research Intern, you will own a 12-week end-to-end project: researching, prototyping, and validating an ML-driven access intelligence engine that combines clustering algorithms and generative AI to surface least-privilege access bundle recommendations for joiners and movers. This work aligns directly with customer demand for smarter access provisioning and runs in parallel with current engineering priorities -- making it an ideal, well-scoped project for an intern to take from concept to working prototype. This is a summer internship that starts in late May and ends in August. Start and end dates are flexible.

Requirements

Strong undergraduate or recent graduate in Computer Science with foundational knowledge of AI/ML and generative AI concepts
You've used LLMs to build something -- in a class, a hackathon, a side project. You understand where they work well and where they don't, and you can talk through the tradeoffs
Solid understanding of supervised and unsupervised learning concepts, feature engineering, and model evaluation. You know how to design a test set and report results honestly
Some exposure to experiment tracking, reproducibility, or structuring ML code for reuse -- whether from coursework, a project, or self-study
You can explain your work clearly to both technical and non-technical audiences, in writing and in conversation
You hold yourself to a high bar, push through ambiguity, and don't consider something done until it's actually good
You can take a scoped project, manage your own progress with periodic check-ins, and see it through to completion

Nice To Haves

Some exposure to identity, access management, or security concepts -- through coursework, reading, or personal interest
Familiarity with graph-based or network-based ML concepts
Coursework or projects touching NLP or large language models
Experience writing technical documentation or research reports

Responsibilities

ML research and prototyping: Research, implement, and compare unsupervised learning approaches (e.g., k-means, hierarchical clustering, graph-based methods) to identify peer-group cohorts from employee attributes and entitlement data
Generative AI integration: Connect an LLM layer that translates ML outputs into human-readable cluster names and recommendation rationales that non-technical administrators can act on
Synthetic data pipeline: Design and generate realistic datasets with employee attributes, entitlement histories, and usage signals to support training, evaluation, and offline testing
Extensible framework: Build a modular, well-documented codebase designed to serve as the foundation for future access intelligence work beyond this internship
Technical report: Document your methodology, experiments, results, and recommendations in a written report that informs the production roadmap
Final presentation: Demo the working prototype and present key learnings to the engineering team and leadership