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The Microsoft Security Organization is building the next generation of security solutions by rethinking how security work is done from the ground up. The AI for Security (AI4S) Team within the Microsoft Threat Protection (MTP) Org is pioneering a new paradigm: leveraging frontier AI models, security specific tooling, knowledge graphs, and deep context engineering to transform how defenders detect, investigate, and respond to threats at global scale. This is a fast-paced, highly interdisciplinary environment focused on real world security outcomes. We are looking for Principal Software Engineers - Applied AI for Microsoft Threat Protection (MTP) to help us in our goal to build intelligent security systems that meaningfully reduce defender toil, improve precision and speed, and unlock entirely new security workflows that were previously impossible.
Job Responsibility:
Design and evolve AI‑driven security systems leveraging large language models, multimodal models, and frontier capabilities to address complex security challenges
Develop contextual knowledge systems, including security graphs, semantic representations, memory frameworks, and high‑quality reasoning over security data
Collaborate across disciplines with Security Engineers, domain experts, and Product Managers to define inclusive, AI‑native security experiences
Partner with AI Infrastructure and Platform teams, Research, and Model Engineering groups to translate security workflows into AI‑optimized architectures
Enable automation, augmentation, and responsible autonomy to drive measurable functional improvements across security solutions
Prototype, validate, and deploy solutions in live production environments while upholding Microsoft standards for security, reliability, privacy, and trust
Shape technical direction for AI Security by influencing architecture, tooling, engineering practices, and shared best practices
Lead cross‑team initiatives spanning security products, platforms, and business units through collaboration and shared ownership
Mentor and sponsor engineers at multiple levels, fostering inclusive technical dialogue, sound engineering judgment, and continuous growth
Contribute to a culture of learning, accountability, and impact across the broader engineering and security community
Requirements:
Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, Python, C#, Go, or Java OR equivalent experience
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter
Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
6+ years of experience designing, building and operating scalable ML systems, including ML infrastructure and pipelines (Azure ML, Kubernetes), model versioning, observability, and secure deployment, with hands‑on experience in MLOps/AIOps/SecDevOps practices
6+ years of experience building secure, reliable software systems, with applied knowledge of authentication, data protection, access control, and secure coding practices
6+ years of experience designing, building, and operating distributed or cloud‑scale systems (Azure, AWS, or GCP), including production ownership, CI/CD integration, and operating highly available services
2+ years of experience designing and building applications with LLM orchestration frameworks (e.g., LangChain, AutoGen), including agent‑based workflows, RAG pipelines, prompt engineering, and model fine‑tuning/evaluation