Natalie
Isak
Pioneering responsible AI development, with a focus on AI safety, privacy-preserving systems, and trustworthy machine learning.
Passion for AI Safety
I am deeply passionate about Artificial Intelligence safety and driven to make a meaningful impact in this critical field. My journey began at Cornell University, where I led a research team developing computer vision models for environmental monitoring and studied bias in information networks under Jon Kleinberg.
My ambition led me to join Microsoft through the Microsoft AI Development Acceleration Program. This opportunity allowed me to contribute to cutting-edge AI research and development initiatives across the company.
When generative AI emerged in 2022, I was invited to join an internal Responsible AI review board for all generative AI releases at Microsoft. The responsibility for assessing safety across dozens of product releases became mine—a role that fundamentally shaped my understanding of AI safety challenges.
Safety First
Designing mitigations before risks manifest
Research
Identifying novel challenges at scale
Top of Mind
- →Understanding and measuring emergent risks without compromising user privacy
- →Detecting harmful content (e.g. cross prompt injections) in multimodal inputs and outputs
Experience
Machine Learning Engineer II
Microsoft AI Safety
New York, NY
- •Architected and executed the first-ever AI-powered retroactive scan of M365 data, using LLM-based detection to search >90 million logs with 99.84% accuracy
- •Co-designed BinaryShield, a privacy-preserving technique enabling cross-compliance boundary searches (patent & publication)
- •Designed and maintained end-to-end processor for M365 data to detect AI safety risks, including Crescendo adversarial attack detector with 96.65% precision
- •Mentored junior engineers and interns on AI safety best practices
Machine Learning Engineer II
Microsoft AI Development Acceleration Program
Cambridge, MA
- •Architected AI data entry agent with >200K MAU, improving latency by 90.72% and saving ~$300K CAD annually
- •Part-time lead on Microsoft's internal Responsible AI review board for companywide AI releases
- •Developed open-source Semantic Kernel agentic framework (awarded 3 independent patents)
- •Designed RAG AI Plugin for M365 Chat projected to alleviate 80% of HR support queue
- •Added full stack support for object detection in RAI Dashboard, released at Microsoft Build
Software Engineering Intern
Microsoft
Remote
- •Implemented dynamic status feature for PSTN endpoint within Microsoft Teams
Researcher
Cornell Netlab
Ithaca, NY
- •Researched intermediate representations for formal verification
- •Composed a pretty printer for Petr4 and designed compiler from Petr4 to C
Data Science Intern
Tesla
Remote
- •Designed automated ML model to predict vehicle order cancellation frequency
- •Created Tableau visualizations for senior engineers and director of analytics
Backend Software Engineering Intern
Uber
Remote
- •Integrated external vendor API for rider verification feature using government-issued ID
- •Implemented fuzzy matching library with extensive integration tests
Education
Cornell University
B.S. Computer Science, 2022
College of Engineering
GPA: 3.70 / 4.3
Dean's List: Fall '18, Spring '21, Fall '21, Spring '22
Research Impact
Contributing to the frontier of AI safety through peer-reviewed publications, patents, and thought leadership.
Privacy-Preserving Fingerprinting for AI Threat Detection and Mitigation
Novel technique for detecting AI threats while preserving user privacy across compliance boundaries.
Cross-Service Threat Intelligence in LLM Services using Privacy-Preserving Fingerprints
Research paper on enabling cross-service threat detection in LLM systems while maintaining privacy.
AI Risks and Mitigations
Women Impact Tech Conference
Keynote presentation on AI risks and practical mitigations to a conference audience of 1,200 attendees.
Artifact Designer for Guided Conversation Artifacts
System for designing and managing conversational AI artifacts with guardrails.
Generatively-guided artifact construction with constraints
Framework for constrained generation of AI artifacts using semantic guardrails.
Cyclic Behavior Detection in Generative Agents
Detection system for identifying and preventing cyclic behaviors in AI agents.
P4Cub: A Little Language for Big Routers
CPP '23
Formal verification research on intermediate representations for network routers.
BinaryShield: Privacy-Preserving Threat Detection
When my team faced the challenge of detecting adversarial attacks on AI systems while navigating customer privacy protections, I architected BinaryShield—a technique for cross-compliance boundary searches. This work exemplifies what excites me most: identifying novel AI safety challenges and rapidly generating solutions with real-world impact.
Expertise
Specialized in building safe, scalable AI systems with a deep understanding of responsible AI practices.
Tools & Technologies
Awards
2nd Place Winner - Executive Challenge Hack
2023 Global Microsoft Hackathon
Revolutionizing Customer Security Scenarios
Intel URP Scholar
Spring 2020
Undergraduate research scholarship recipient
Rewriting The Code Fellow
2020-2021
Fellowship for women in technology
Lockheed Martin Corporate Award
Spring 2022
Recognition for excellence in engineering
Leadership
Research Advisor
AguaClara Project Team
Ithaca, NY
Led three sub-teams (~15 people) developing an app to measure effectiveness of water purifying techniques.
Executive Board Member
Women In Computing At Cornell
Ithaca, NY
Organized ~8 inclusivity events per semester, including the sold-out CIS formal under budget of $7,750.
Head Consultant
Cornell Intro to CS Class
Ithaca, NY
Supervised ~60 undergraduate teaching assistants. Designed coursework and graded assignments.
Volunteering & Teaching
For Fun
Marathon
Completed Philly in '24
Recent Travels: Iceland
Get in Touch
Interested in discussing AI safety, responsible AI development, or potential collaborations? I'd love to hear from you.