Overview
Microsoft Advertising serves billions of ad decisions every day across search, native, shopping, display, audience, and AI-powered advertising experiences. Trust & Safety is foundational to this marketplace: protecting users from harmful, deceptive, and low-quality content; protecting advertisers, publishers, and partners from fraud and abuse; and ensuring Microsoft Ads operates with strong policy, privacy, security, compliance, and regulatory integrity.We are seeking a deeply technical Senior Principal Architects to define and drive the next generation of the Ads Trust & Safety AI Platform. This is a engineering leadership role that entails setting technical direction, shaping platform strategy, influencing multiple engineering and science teams, and building durable systems that compound across Trust & Safety, Risk, Fraud, Security, Policy, Measurement, and Enforcement. This role is for a hands-on technical leader who can combine large-scale platform engineering with modern AI systems: agentic investigation workflows, LLM-powered reasoning, retrieval and evidence generation, heterogeneous model serving, risk intelligence, human-in-the-loop review, and high-integrity decisioning.The platform will power how Microsoft Ads understands advertisers, domains, landing pages, websites, business entities, policy risk, fraud signals, and adversarial behavior. It will enable faster and more reliable enforcement decisions, richer evidence for reviewers and investigators, safer automation, and stronger marketplace protection. A key part of this role is collaboration across the broader Trust ecosystem: Microsoft Ads, Microsoft Trust and Safety teams across products, Security, Identity, and relevant worldwide industry peers and partners. The role requires someone who can evangelize technical direction, learn from adjacent teams and industry practices, align standards, and convert shared learnings into production-grade platform capabilities.
Responsibilities
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Define the long-term architecture for the Ads Trust & Safety AI Platform across ingestion, signal acquisition, entity intelligence, retrieval, model orchestration, agentic workflows, decisioning, enforcement, human review, audit, and measurement.
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Translate broad Trust & Safety, Risk, Fraud, Security, and Policy needs into reusable platform capabilities.
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Establish reference architectures, design principles, technical standards, and engineering patterns for high-integrity AI and decisioning systems.
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Drive architecture choices across latency, throughput, quality, cost, explainability, governance, reliability, and operational safety.
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Identify critical platform gaps and create a roadmap that balances near-term delivery with long-term leverage.
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Architect platform for Deep Research Agents that investigate domains, landing pages, advertisers, business entities, ownership patterns, web presence, reputation, policy risk, and fraud signals.
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Architect workflows that combine retrieval, crawling, structured evidence extraction, LLM reasoning, policy grounding, risk scoring and human in the loop.
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Architect guardrails for agentic systems, including source provenance, confidence scoring, hallucination controls, audit logs, escalation paths, and human override.
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Partner with Applied Science to convert AI research prototypes into production systems with clear quality, latency, cost, reliability, safety, and governance targets.
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Architect systems for high-fidelity understanding of domains, websites, landing pages, advertisers, business identities, ownership structures, relationship graphs, reputation, and provenance.
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Design real-time, nearline, and batch scoring systems for policy enforcement, fraud detection, abuse prevention, advertiser risk scoring, and marketplace protection.
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Evolve abstractions for model orchestration, feature lookup, signal stores, retrieval, model versioning, decision logging, policy controls, fallbacks, and experimentation.
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Architect systems to detect , learn and mitigate adversarial behavior across the advertiser lifecycle, including account creation, login events, payment changes, budget changes, campaign edits, creative changes, landing-page changes, and enforcement history.
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Build sequential and event-based risk systems that reason over advertiser behavior over time rather than treating each decision as an isolated event.
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Collaborate across wider Microsoft , Safety, Security, Responsible AI, Identity and partner teams to create shared platform capabilities for risk detection, abuse prevention, evidence generation, and enforcement governance.
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Evangelize a coherent Trust & Safety AI platform strategy across Microsoft, helping teams converge on shared architectures, reusable abstractions, common taxonomies, and consistent decisioning patterns.
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Evangelize & Learn from industry peers, and approved partner networks facing similar scale abuse patterns, and translate those learnings into practical platform improvements.
Qualifications
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Bachelor’s Degree in Computer Science or related technical field AND 15+ years of professional software engineering experience, or equivalent practical experience.8+ years of senior technical leadership experience influencing engineers, technical leads, architects, applied scientists, or cross-functional engineering teams across complex platform or product areas.
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Proven experience architecting and delivering large-scale production systems with meaningful reliability, scalability, latency, correctness, availability, security, and operational requirements.
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Deep technical experience in one or more of the following platform areas: AI/ML systems, agentic systems, model-serving infrastructure, decisioning systems, distributed systems, data platforms, workflow platforms, risk platforms, security platforms, or Trust & Safety systems.
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Experience building or technically leading production AI/ML systems, including exposure to modern AI patterns such as LLM-based workflows, retrieval-augmented generation, model orchestration, automated reasoning, human-in-the-loop systems, AI-assisted operational tooling, or agentic workflows.
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Strong understanding of the 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.
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Experience designing high-integrity systems where decisions must be auditable, reproducible, explainable, governed, and secure, especially when handling sensitive signals, advertiser impact, policy enforcement, risk decisions, or compliance-sensitive workflows.
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Ability to drive clarity from ambiguity, define technical direction, create reusable platform abstractions, and influence execution across multiple teams without relying on direct management authority.
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Strong written and verbal communication skills, including the ability to explain architecture, tradeoffs, risks, sequencing, and technical strategy to senior engineering, science, product, policy, security, and business leaders.
Preferred Qualification
The candidate is not expected to have deep experience in every area below. Broader coverage across these areas is preferred, especially where the candidate has demonstrated the ability to connect multiple domains into durable platform architecture and production systems.
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Deep domain experience in Trust & Safety, Fraud, Abuse, Risk, Security, Ads Quality, Marketplace Integrity, Policy Enforcement, or advertiser protection systems.Experience with adversarial systems, including phishing, malware, cloaking, account takeover, payment abuse, fake identities, compromised advertisers, coordinated fraud, and policy evasion.
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Experience building Deep Research Agents, investigation agents, reviewer-assist systems, retrieval-augmented generation systems, LLM-powered operational workflows, or AI systems that produce grounded evidence for human or automated decisions.
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Expertise in heterogeneous inference platforms supporting LLMs, SLMs, wide & deep models, ensembles, graph models, classical ML models, heuristics, and rules engines.
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Experience with entity intelligence, knowledge graphs, web crawling, domain reputation, business identity resolution, provenance, evidence extraction, or risk scoring.
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Experience designing human-in-the-loop review systems, appeals workflows, audit platforms, policy reasoning systems, or enforcement governance mechanisms.
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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.
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Experience collaborating with Trust, Safety, Security, Privacy, Identity, Compliance, Legal, or Responsible AI teams across multiple products or platforms.
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Experience evangelizing technical strategy across multiple teams, learning from industry peers, and helping establish shared standards, taxonomies, schemas, signal-quality measures, or platform patterns.
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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.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process.
