Member of Technical Staff - Privacy Engineer, Health

Overview

Microsoft AI (MAI) is building consumer health experiences - including Copilot Health - that bring trustworthy, AI-powered guidance to millions of people. Health information is among the most sensitive data anyone shares, and the trust people place in us depends on getting privacy, security, and regulatory compliance right from the very first line of code.

We are looking to hire our engineering team’s anchor for all things privacy, compliance, and regulatory. This is a senior individual-contributor role for a hands-on engineer who can set technical direction, build privacy-preserving systems, and serve as the trusted point of contact who keeps our products on the right side of a fast-evolving regulatory landscape. You will work at the intersection of privacy engineering, health technology, and applied AI - partnering closely with product, security, legal, and compliance to make privacy-by-design the default rather than an afterthought.

If you are energized by hard problems where protecting people and shipping great products are equally non-negotiable, this role offers outsized impact on how responsible AI in health is built.

Responsibilities

• Be the primary point of contact for privacy, compliance, and regulatory matters within the engineering team - the person product and engineering turn to for guidance, design review, and decisions.

• Ensure products such as Copilot Health are designed and built in a privacy-preserving way, embedding data minimization, purpose limitation, and privacy-by-design into architecture from the outset.

• Translate complex health and data-protection regulations - including UK and EU GDPR, HIPAA, and the EU AI Act - into concrete technical requirements, engineering guardrails, and automated, continuously verifiable controls.

• Lead privacy and security design reviews and threat modeling for new features and models, identifying risks early and architecting practical, scalable mitigations.

• Design and build foundational privacy infrastructure: data classification, policy-driven access controls, consent and preference management, audit logging, data lineage, and retention and lifecycle controls.

• Evaluate and apply privacy-enhancing technologies - such as differential privacy, de-identification, secure enclaves, and federated approaches - where they meaningfully reduce risk to users.

• Build reusable libraries, patterns, and tooling that let every engineer ship privacy-preserving features by default, and raise the team’s privacy and compliance fluency through mentorship and clear standards.

• Partner across legal, compliance, security, and product to balance strong user protections with product velocity, and to support audits, certifications, and regulator-facing evidence.