ML / AI Research Intern

Oleria provides adaptive and autonomous identity security solutions to protect and enable today's dynamic enterprise. Our platform delivers unparalleled fine-grained access visibility, including usage insights at the individual resource level, so CISOs can confidently answer: Who has access to what? How did they get it? What are they doing with it?

With our recent launch of Adaptive Identity Governance, we have a massive opportunity to disrupt the legacy IGA market. We're replacing multi-year, multi-million dollar deployments with a data-first, AI-native platform that gets organizations governed in under an hour. It's a wide-open market, and we're just getting started.

Founded by cybersecurity veterans Jim Alkove and Jagadeesh Kunda, who built and secured platforms used by billions globally, we've raised $60M from Evolution Equity Partners, Salesforce Ventures, Tapestry VC, Zscaler, and other technology luminaries. Our Fortune 500 and public sector clients trust us to protect millions of users worldwide.

We're proud to be recognized as a LinkedIn Best Startup and a Best Startup to Work For by Built In. Join a well-funded team with real traction, a game-changing new product, and a mission to deliver identity security that sets your business free.

Learn more: www.oleria.com

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. 

You'll own this project end-to-end -- from research and data pipeline through working prototype and final presentation. Core work includes:

• 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

• CS foundation
  Strong undergraduate or recent graduate in Computer Science with foundational knowledge of AI/ML and generative AI concepts
• Applied AI curiosity
  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
• ML fundamentals
  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
• MLOps awareness
  Some exposure to experiment tracking, reproducibility, or structuring ML code for reuse -- whether from coursework, a project, or self-study
• Communication
  You can explain your work clearly to both technical and non-technical audiences, in writing and in conversation
• Drive and ownership
  You hold yourself to a high bar, push through ambiguity, and don't consider something done until it's actually good
• Self-directed
  You can take a scoped project, manage your own progress with periodic check-ins, and see it through to completion

• 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

We believe in investing in the people who power our mission -- including the next generation of engineers and researchers. Here's what you'll get:

• Mentorship from builders
  You'll work directly with an Architect, a Product Manager, and a senior Engineering Mentor who will give you day-to-day engineering guidance, code reviews, and career coaching throughout the engagement
• Real ownership
  This isn't a shadow role. You'll own the project end-to-end -- from data pipeline through prototype to final demo -- with a tangible artifact to show for it
• Production-grade experience
  You'll work on a real, open engineering problem in a production-oriented environment, gaining hands-on experience with applied ML, generative AI, and identity security that goes beyond what coursework alone can offer
• Durable impact
  Your codebase, evaluation framework, and technical report become permanent Oleria assets that accelerate future engineering investment. Your work shapes what goes on the roadmap next
• A culture that brings out your best
  Join a team where everyone takes initiative and accountability for our shared success. You'll be surrounded by colleagues who ask bold questions, challenge assumptions, and never stop learning