Principal Software Engineering Manager | Ads Trust & Safety AI Platform

What you'll do

• Lead and grow a highly technical engineering team responsible for core areas of the Ads Trust & Safety AI Platform including advertiser/domain intelligence, content moderation, AI-assisted investigation, decisioning, enforcement, human review, observability, and risk/fraud protection
• Set technical direction and execution priorities across platform delivery, quality, reliability, operational excellence, and live-site readiness
• Hire, coach, and develop engineers and technical leads to grow in architecture, execution, production quality, ownership, and technical judgment
• Own delivery for major platform components across ingestion, signal processing, entity intelligence, model serving, content moderation, decisioning, enforcement, audit, and measurement
• Partner with senior ICs, architects, Applied Science, Product, Policy, Security, Identity, and partner engineering teams to translate business needs into scalable platform capabilities
• Drive productionization of AI/ML and agentic capabilities with quality, latency, cost, reliability, and safety mechanisms
• Deliver real-time, nearline, and batch decisioning systems for policy enforcement, content moderation, fraud detection, abuse prevention, advertiser risk scoring, and marketplace protection
• Evangelize the team’s platform capabilities across Microsoft Ads and relevant Microsoft Trust teams to help partner teams adopt shared systems

Requirements

• Bachelor’s Degree in Computer Science or related technical field
• 8+ years of engineering management experience leading software engineering teams
• Experience managing senior engineers, technical leads, and managers
• Experience building and operating large-scale production systems with reliability, scalability, latency, correctness, availability, security, and operational requirements
• Technical depth in AI/ML systems, model-serving infrastructure, decisioning systems, distributed systems, data platforms, workflow platforms, risk platforms, security platforms, or Trust & Safety systems
• Experience partnering with Applied Science, Product, or cross-functional stakeholders to translate ambiguous requirements into shipped engineering systems
• Exposure to production AI/ML systems including LLM-based workflows, retrieval-augmented generation, model orchestration, automated reasoning, human-in-the-loop systems, AI-assisted operational tooling, or agentic workflows
• Understanding of production requirements for AI or decision systems including evaluation, observability, quality measurement, rollout safety, fallback behavior, latency/cost tradeoffs, explainability, governance, and operational reliability
• Experience building high-integrity systems with auditable, reproducible, explainable, governed, and secure decisions
• Ability to hire, grow, coach, and retain strong engineering talent
• Strong communication skills to explain technical tradeoffs, risks, execution plans, and platform strategy to diverse stakeholders
• Preferred: Experience in Trust & Safety, Fraud, Abuse, Risk, Security, Ads Quality, Marketplace Integrity, Policy Enforcement, or advertiser protection systems
• Preferred: Experience with adversarial systems such as phishing, malware, cloaking, account takeover, payment abuse, fake identities, compromised advertisers, coordinated fraud, and policy evasion
• Preferred: Experience managing teams building Deep Research Agents, investigation agents, reviewer-assist systems, retrieval-augmented generation systems, LLM-powered operational workflows, or AI systems producing grounded evidence
• Preferred: Experience managing teams building model-serving, decisioning, enforcement, workflow, or human-review platforms
• Preferred: Experience with entity intelligence, knowledge graphs, web crawling, domain reputation, business identity resolution, provenance, evidence extraction, or risk scoring
• Preferred: Experience designing or operating human-in-the-loop review systems, appeals workflows, audit platforms, policy reasoning systems, or enforcement governance mechanisms

Tech stack