Senior Principal Architect : Ads Trust & Safety AI Platform

AI Platform Architecture and Technical Strategy 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. Translate broad 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. Drive architecture choices across latency, throughput, quality, cost, explainability, governance, reliability, and operational safety. Identify critical platform gaps and create a roadmap that balances near-term delivery with long-term leverage. Architect platform for Deep Research Agents that investigate domains, landing pages, advertisers, business entities, ownership patterns, web presence, reputation, policy risk, and fraud signals. Architect workflows that combine 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. 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. 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. Build sequential and event-based risk systems that reason over advertiser behavior over time rather than treating each decision as an isolated event. 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. Proven experience architecting and delivering large-scale production systems with meaningful reliability, scalability, latency, correctness, availability, security, and operational requirements. 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. 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. 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. 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. 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. 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. 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. 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. 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. Expertise in heterogeneous inference platforms supporting LLMs, SLMs, wide & deep models, ensembles, graph models, classical ML models, heuristics, and rules engines. Experience with entity intelligence, knowledge graphs, web crawling, domain reputation, business identity resolution, provenance, evidence extraction, or risk scoring. Experience designing human-in-the-loop review systems, appeals workflows, audit platforms, policy reasoning systems, or enforcement governance mechanisms. 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. Experience collaborating with Trust, Safety, Security, Privacy, Identity, Compliance, Legal, or Responsible AI teams across multiple products or platforms. Experience evangelizing technical strategy across multiple teams, learning from industry peers, and helping establish shared standards, taxonomies, schemas, signal-quality measures, or platform patterns. 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.