Principal Software Engineer, Perception Platform and AI Systems

Microsoft•Redmond, WA

4d•$142,800 - $304,200

About The Position

Security represents one of the most critical priorities for our customers in a world of evolving digital threats, increasing regulatory requirements, growing estate complexity, and rapid advances in AI. Microsoft Security aspires to make the world a safer place by empowering every user, customer, and developer with integrated security solutions that protect across heterogeneous environments. Perception is an advanced AI platform designed to empower security and IT teams to operate at the speed and scale of AI while adhering to Microsoft's responsible AI principles. We are looking for a Principal Software Engineer to provide technical leadership for the evolution of the Perception Platform and drive foundational AI and platform investments across Microsoft Security. You will define architecture and engineering strategy for sophisticated Generative AI, Retrieval-Augmented Generation (RAG), memory, reasoning, agentic and multi-agent systems, identity and authorization, AI safety, and distributed cloud platforms operating in security-sensitive enterprise environments. Combining deep distributed systems and cloud platform expertise with strong AI systems architecture experience, you will establish architectural patterns that enable multiple engineering teams and Microsoft Security products to build secure, reliable, compliant, and scalable AI-powered experiences on common platform foundations. You will operate across organizational and product boundaries to translate ambiguous business and product requirements into durable technical architectures, drive alignment among senior technical leaders, and identify opportunities for MSEC-wide engineering leverage. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Requirements

Bachelor'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, Go, JavaScript, or Python 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 background and Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

Master'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, Go, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, Go, JavaScript, or Python OR equivalent experience.
Extensive experience architecting, building, and operating large-scale distributed systems, cloud services, enterprise platforms, or AI platforms in production environments.
Deep experience with Generative AI and AI systems architecture, including model orchestration, retrieval/RAG, semantic search, memory, context management, tool use, evaluation, safety, and production operationalization.
Experience designing agentic and multi-agent AI systems, including planning, reasoning, orchestration, delegation, collaboration, state and memory management, tool use, long-running execution, and failure recovery.
Experience establishing reusable engineering patterns, frameworks, and platform capabilities for reliable, secure, observable, and scalable AI and agentic systems.
Deep experience designing identity, authentication, and authorization architectures for autonomous or agentic systems, including user, application, workload, service, and agent identities.
Expertise in RBAC, policy-based and resource-level authorization, least privilege, scoped permissions, delegated authorization, and multi-tenant isolation, including securing delegation chains and preventing privilege escalation or unauthorized cross-tenant access.
Demonstrated ability to provide technical leadership across complex, ambiguous initiatives, translating business, product, and security requirements into durable architectures, technical standards, and long-term engineering strategies across multiple teams or organizations.
Demonstrated technical leadership and communication skills, with experience influencing architectural decisions across organizational boundaries, mentoring senior engineers, establishing engineering standards, and communicating complex technical decisions, risks, and tradeoffs to engineering and leadership audiences.

Responsibilities

Define the architecture and long-term technical strategy for enterprise AI platform capabilities spanning generative AI, retrieval, memory, reasoning, agent orchestration, model interaction, execution, evaluation, extensibility, security, and safety.
Architect production-grade AI and Retrieval-Augmented Generation (RAG) systems, including data ingestion, indexing, embeddings, semantic and hybrid retrieval, context construction, grounding, provenance, re-ranking, and retrieval evaluation.
Design enterprise AI memory and knowledge architectures that manage conversational context, durable memory, organizational knowledge, and retrieved information while addressing authorization, provenance, freshness, retention, correction, deletion, and compliance requirements.
Architect agentic and multi-agent AI systems that support planning, reasoning, tool use, task decomposition, delegation, collaboration, state and memory management, human-in-the-loop patterns, failure recovery, and bounded autonomous execution.
Define identity, authentication, and authorization architectures for AI agents, including user, workload, service, and agent identities; Role-Based Access Control (RBAC); delegated authorization; least-privilege access; multi-tenant isolation; and secure propagation of identity and authorization context across distributed workflows.
Establish security, safety, and execution guardrails that protect AI systems against prompt injection, malicious or untrusted content, unsafe tool invocation, data exfiltration, privilege escalation, unauthorized delegation, excessive agency, and cross-tenant access.
Design AI architectures that enforce authorization and trust throughout retrieval and execution, ensuring agents access only permitted data and that AI-generated plans, decisions, and tool calls are independently validated against applicable security, authorization, safety, and compliance policies.
Establish end-to-end reliability, evaluation, and observability frameworks for distributed AI systems, covering retrieval and model quality, grounding, agent behavior, authorization decisions, tool execution, task completion, safety, telemetry, regression detection, failure recovery, and graceful degradation.
Partner across engineering, research, product, security, privacy, and compliance organizations to translate evolving AI capabilities, risks, and enterprise requirements into scalable architectures and balance quality with reliability, latency, cost, security, safety, and operational complexity.
Provide Principal-level technical leadership across Microsoft Security AI platform investments, driving architectural convergence, reusable APIs and frameworks, reference architectures, technical design reviews, foundational investments, and alignment across organizations while mentoring senior engineers and technical leaders.