Principal Software Engineer, Perception Platform and AI Systems

Microsoft•Redmond, WA

7d•$142,800 - $304,200

About The Position

Microsoft Security is seeking a Principal Software Engineer to provide technical leadership for the evolution of its Perception AI platform and drive foundational AI and platform investments across Microsoft Security. The role defines architecture and engineering strategy for 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. The engineer will combine distributed-systems and cloud-platform expertise with AI systems architecture experience to establish patterns that let multiple engineering teams and Microsoft Security products build secure, reliable, compliant, and scalable AI-powered experiences on shared foundations. The role works across organizational and product boundaries, translating ambiguous business and product needs into durable technical architectures, aligning senior technical leaders, and identifying opportunities for Microsoft Security-wide engineering leverage. Microsoft describes its mission as empowering every person and organization to achieve more, and emphasizes growth mindset, innovation, collaboration, respect, integrity, accountability, and inclusion.

Requirements

Bachelor's degree in Computer Science or a related technical field and 6+ years of technical engineering experience coding in languages such as C, C++, C#, Java, Go, JavaScript, or Python, or equivalent experience.
Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Nice To Haves

Master's degree in Computer Science or a related technical field and 8+ years of technical engineering experience coding in languages such as C, C++, C#, Java, Go, JavaScript, or Python; or a bachelor's degree and 12+ years of such experience; or equivalent experience.
Extensive experience architecting, building, and operating large-scale distributed systems, cloud services, enterprise platforms, or AI platforms in production.
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, including 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 architecture and long-term technical strategy for enterprise AI platform capabilities, including generative AI, retrieval, memory, reasoning, agent orchestration, model interaction, execution, evaluation, extensibility, security, and safety.
Architect production-grade AI and RAG systems, including 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.
Architect agentic and multi-agent systems supporting 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; RBAC; delegated authorization; least privilege; multi-tenant isolation; and secure propagation of identity and authorization context across distributed workflows.
Establish security, safety, and execution guardrails 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 AI-generated plans, decisions, and tool calls are validated against 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 with engineering, research, product, security, privacy, and compliance organizations to translate evolving AI capabilities, risks, and enterprise requirements into scalable architectures and balance quality, reliability, latency, cost, security, safety, and operational complexity.
Provide Principal-level technical leadership across Microsoft Security AI platform investments by driving architectural convergence, reusable APIs and frameworks, reference architectures, technical design reviews, foundational investments, and organizational alignment, while mentoring senior engineers and technical leaders.