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 Experience developing privacy risk measurement, model evaluation, privacy red teaming, or control validation methods. Experience translating privacy research, measurement methods, or experimental techniques into scalable engineering systems and practices. Experience working with engineering teams to make tradeoffs between privacy risk, technical feasibility, product value, and data utility. Experience building or influencing data governance systems, including lineage, provenance, access governance, retention, deletion, consent enforcement, opt-out enforcement, or dataset eligibility controls. Experience working in ambiguous technical areas where there is not already a standard playbook. Experience designing or implementing privacy-enhancing technologies or privacy measurement techniques for machine learning systems. Experience working across research and production engineering environments and translating experimental methods into scalable engineering practices. Applied Sciences IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.