Privacy Research Engineer, AI

Overview

Microsoft AI is building the models and systems that will define the next generation of AI products. That work depends on making decisions about data: what data we can use, how it should be prepared, what risks it creates, what controls are sufficient, and how we prove those controls work. 

We are looking for a Privacy Member of Technical Staff to work directly with the engineers, researchers, and platform teams training and evaluating Microsoft AI models. This is a deeply technical role for someone who understands model development, privacy risk, and large-scale data systems, and can help scale sound engineering decisions about data use before risks are baked into the system. 

This role will help define how privacy is engineered into AI development. They will develop technical approaches for assessing and measuring risks like memorization, regurgitation, membership inference, model inversion, reconstruction, and training data leakage. They will evaluate whether mitigations such as scrubbing, loss masking, aggregation, de-identification, differential privacy, synthetic data, or access controls are sufficient for a specific dataset, model type, training method, or use case. They will also help architect the data governance, storage, lineage, retention, deletion, and access control systems that make compliant AI development possible at scale. 

This is not a traditional privacy review role. The right person will be embedded with teams training models, asking and answering questions like: How do we quantify the privacy risk of this dataset? Does this training approach change the risk profile? Is de-identification enough here, or do we need stronger controls? What evidence do we need before we can say a mitigation works? How do we preserve model utility while reducing privacy risk in a measurable way? 

Job Responsibilities

• Partner directly with engineers, researchers, and platform teams on model training, evaluation, data preparation, and AI system architecture. 
• Define technical privacy requirements for model training and AI development, including requirements for datasets, storage, access, retention, deletion, opt-out enforcement, data subject right readiness, and downstream use. 
• Develop and apply methods to assess and quantify privacy risks in AI systems, including memorization, regurgitation, membership inference, reconstruction, model inversion, and training data leakage. 
• Evaluate how privacy risk changes across datasets, model architectures, training approaches, and privacy-preserving techniques.  
• Determine the effectiveness and limitations of mitigations such as scrubbing, loss masking, aggregation, de-identification, differential privacy, filtering, or synthetic data, and when additional controls are required. 
• Develop measurable privacy criteria for AI systems, including risk thresholds, test methods, validation requirements, and evidence expectations. 
• Help architect privacy and data governance infrastructure for AI development, including data lineage, provenance, approved use tracking, purpose limitation, access controls, auditability, retention, and deletion. 
• Translate privacy, legal, regulatory, and policy requirements into engineering requirements that can be implemented and tested. 
• Build repeatable patterns so teams can move faster with data that is governed, measurable, and ready for model development. 
• Represent privacy in technical design discussions where model quality, data utility, privacy risk, and engineering feasibility need to be balanced. 

Required Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 
• 4+ year of experience with privacy-preserving machine learning or data protection techniques, such as differential privacy, de-identification, aggregation, synthetic data, data minimization, sensitive data filtering, or loss masking.
• 3+ years of technical experience assessing privacy risks in AI or machine learning systems, including one or more of the following: memorization, regurgitation, membership inference, reconstruction, model inversion, or training data leakage.
• Familiarity with privacy requirements and emerging AI governance expectations under GDPR, UK GDPR, EU AI Act, DSA, CCPA/CPRA, or similar regulatory frameworks.

Preferred Qualifications