Principal AI Engineer

Role

Principal AI Engineer on Microsoft Security NEXT (incubation and research arm of Microsoft Security AI). Own the technical vision, strategy, and roadmap for AI-native security initiatives; lead incubation from prototype to MVP and then platformization and scale across Microsoft security products.

Key Responsibilities

• Define technical vision, strategy, and roadmap for AI-native security experiences and platform components.
• Lead zero-to-one (0→1) incubation through MVP and private preview, then drive one-to-many (1→N) platformization and GA scale.
• Provide hands-on technical leadership: prototype in code, review designs and PRs, define APIs/data contracts, and build well-architected systems.
• Set strategy for where to use LLMs vs classical ML, design retrieval/RAG, grounding, model routing/fallbacks, and safety guardrails.
• Ensure Responsible AI, privacy, and security guardrails are integrated from day one; coordinate safety reviews, abuse prevention, compliance, and incident readiness.
• Lead and mentor v-teams, cultivate a builder culture, and engage with enterprise customers and field teams to co-design and drive adoption.
• Communicate program status and strategy to executives with hands-on code demonstrations.

Requirements

Required

• Bachelor’s degree in Computer Science or related technical field AND 6+ years technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
• Ability to meet Microsoft and customer security screening requirements (Microsoft Cloud background check and recurring checks).

Preferred

• Master’s degree plus 8+ years experience, or Bachelor’s plus 12+ years experience, or equivalent.
• 6+ years driving complex, cross-functional initiatives and leading without authority across teams.
• 3+ years working with ML/AI systems including LLMs/GenAI, retrieval/RAG, model serving, experimentation platforms, and data pipelines.
• Experience shipping cloud services/platforms at scale (multi-tenant, high-throughput) with measurable impact.
• Security domain expertise (threat detection/response, SIEM/SOAR, identity, endpoint, cloud security) and familiarity with analyst workflows.
• Experience with GenAI/LLM techniques and tooling (prompt engineering, retrieval/vector stores, agents/tool use, content safety/guardrails, eval frameworks, vibe coding).
• Hands-on coding in one or more languages (Python, C#, C++, Rust, JavaScript/TypeScript); comfortable prototyping and participating in design reviews.
• Demonstrated success taking ideas from ambiguity to MVP to GA and platform adoption across multiple product teams.

What Makes a Strong Candidate

• Model-literate and pragmatic: knows when to use LLMs vs deterministic or classical ML and how to hybridize approaches to meet SLOs and cost targets.
• Evaluation-obsessed: defines metrics and datasets, builds eval harnesses, and demands measurable improvements before broad rollout.
• Zero-to-one builder and one-to-many integrator: able to define durable data/API contracts and negotiate dependencies to meet delivery and operational readiness.
• Customer-focused and outcome-driven: translates workflows into specs, lands adoption, and iterates based on feedback and telemetry.
• Clear communicator: creates clarity in ambiguity and aligns diverse stakeholders across research, engineering, design, and field teams.

Compensation and Miscellaneous

• Typical U.S. base pay range listed: USD 139,900 - 274,800 per year. Different ranges apply for specific locations (San Francisco Bay area and New York City metro: USD 188,000 - 304,200 per year).
• Role may be subject to Microsoft Cloud background checks and recurring screening.