Microsoft Advertising seeks a Senior Principal Architect to lead the Ads Trust & Safety AI Platform. This engineering leadership role involves defining technical direction, shaping platform strategy, and building scalable AI systems for Trust & Safety, Risk, Fraud, Security, Policy, and Enforcement. Responsibilities include architecting platform components such as ingestion, signal acquisition, entity intelligence, model orchestration, agentic workflows, decisioning, enforcement, human review, audit, and measurement. The role requires collaboration across Microsoft teams and industry partners to create shared platform capabilities and evangelize technical strategy. Qualifications include a Bachelor's degree or equivalent, 15+ years software engineering experience, 8+ years senior technical leadership, expertise in AI/ML systems, decisioning systems, and building large-scale production systems with reliability, security, and operational excellence. Preferred experience includes Trust & Safety domains, adversarial systems, Deep Research Agents, heterogeneous inference platforms, knowledge graphs, human-in-the-loop review systems, and collaboration with security and compliance teams.
What you'll do
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Define long-term architecture for Ads Trust & Safety AI Platform including ingestion, signal acquisition, entity intelligence, retrieval, model orchestration, agentic workflows, decisioning, enforcement, human review, audit, and measurement
Translate Trust & Safety, Risk, Fraud, Security, and Policy needs into reusable platform capabilities
Establish reference architectures, design principles, technical standards, and engineering patterns for high-integrity AI and decisioning systems
Identify platform gaps and create roadmap balancing near-term delivery with long-term leverage
Architect platform for Deep Research Agents investigating domains, landing pages, advertisers, business entities, ownership patterns, web presence, reputation, policy risk, and fraud signals
Architect workflows combining retrieval, crawling, structured evidence extraction, LLM reasoning, policy grounding, risk scoring, and human in the loop
Architect guardrails for agentic systems including source provenance, confidence scoring, hallucination controls, audit logs, escalation paths, and human override
Partner with Applied Science to convert AI research prototypes into production systems meeting quality, latency, cost, reliability, safety, and governance targets
Architect systems for high-fidelity understanding of domains, websites, landing pages, advertisers, business identities, ownership structures, relationship graphs, reputation, and provenance
Design real-time, nearline, and batch scoring systems for policy enforcement, fraud detection, abuse prevention, advertiser risk scoring, and marketplace protection
Evolve abstractions for model orchestration, feature lookup, signal stores, retrieval, model versioning, decision logging, policy controls, fallbacks, and experimentation
Architect systems to detect, learn and mitigate adversarial behavior across advertiser lifecycle including account creation, login events, payment changes, budget changes, campaign edits, creative changes, landing-page changes, and enforcement history
Build sequential and event-based risk systems reasoning over advertiser behavior over time
Collaborate across Microsoft Trust, Safety, Security, Responsible AI, Identity and partner teams to create shared platform capabilities
Evangelize Trust & Safety AI platform strategy across Microsoft to converge on shared architectures, reusable abstractions, common taxonomies, and consistent decisioning patterns
Evangelize and learn from industry peers and partner networks to translate learnings into platform improvements
Requirements
Bachelor’s Degree in Computer Science or related technical field or equivalent practical experience
15+ years of professional software engineering experience
8+ years of senior technical leadership experience
Proven experience architecting and delivering large-scale production systems with reliability, scalability, latency, correctness, availability, security, and operational requirements
Deep technical experience in AI/ML systems, agentic systems, model-serving infrastructure, decisioning systems, distributed systems, data platforms, workflow platforms, risk platforms, security platforms, or Trust & Safety systems
Experience building or leading 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
Strong understanding of engineering requirements for deploying AI or decision systems in production including evaluation, observability, quality measurement, rollout safety, fallback behavior, latency/cost tradeoffs, drift detection, explainability, governance, and operational reliability
Experience designing high-integrity systems with auditable, reproducible, explainable, governed, and secure decisions especially for sensitive signals, advertiser impact, policy enforcement, risk decisions, or compliance-sensitive workflows
Ability to drive clarity from ambiguity, define technical direction, create reusable platform abstractions, and influence execution across multiple teams without direct management authority
Strong written and verbal communication skills to explain architecture, tradeoffs, risks, sequencing, and technical strategy to senior leaders
Preferred: Deep domain experience in Trust & Safety, Fraud, Abuse, Risk, Security, Ads Quality, Marketplace Integrity, Policy Enforcement, or advertiser protection systems
Preferred: Experience with adversarial systems including phishing, malware, cloaking, account takeover, payment abuse, fake identities, coordinated fraud, and policy evasion
Preferred: Experience building Deep Research Agents, investigation agents, reviewer-assist systems, retrieval-augmented generation systems, LLM-powered operational workflows, or AI systems producing grounded evidence
Preferred: Expertise in heterogeneous inference platforms supporting LLMs, SLMs, wide & deep models, ensembles, graph models, classical ML models, heuristics, and rules engines
Preferred: Experience with entity intelligence, knowledge graphs, web crawling, domain reputation, business identity resolution, provenance, evidence extraction, or risk scoring
Preferred: Experience with large-scale measurement systems for false positives, false negatives, model drift, agent quality, policy quality, reviewer quality, enforcement stability, business impact, and operational health
Preferred: Experience collaborating with Trust, Safety, Security, Privacy, Identity, Compliance, Legal, or Responsible AI teams across multiple products or platforms
Preferred: Experience evangelizing technical strategy across multiple teams, learning from industry peers, and helping establish shared standards, taxonomies, schemas, signal-quality measures, or platform patterns
Preferred: Experience working with industry partners, trusted abuse-prevention networks, threat-intelligence providers, domain-reputation providers, identity-verification providers, payment-risk partners, or ecosystem safety initiatives
