Principal Software Engineering Manager | Ads Trust & Safety AI Platform

AI-powered Trust & Safety intelligence for understanding advertisers, domains, websites, landing pages, business identities, reputation, provenance, and policy/fraud risk. Deep Research Agents and AI-assisted workflows for advertiser investigation, domain review, evidence gathering, policy reasoning, reviewer assistance, appeals, and fraud analysis. Decisioning and enforcement systems for policy enforcement, fraud detection, abuse prevention, advertiser risk scoring, and marketplace protection. Model-serving and orchestration infrastructure for LLMs, SLMs, wide & deep models, ensembles, classical ML, heuristics, rules, and policy controls. Security, Risk, and Fraud systems for threats such as phishing, malware, cloaking, account takeover, payment abuse, fake business identities, compromised advertisers, coordinated abuse, and policy evasion. Human-in-the-loop, audit, and governance systems with explainability, reproducibility, reviewability, and compliance readiness. Measurement and observability systems to track enforcement quality, false positives, false negatives, latency, cost, drift, reviewer burden, advertiser impact, and live-site health. 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, helping them 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 clear quality, latency, cost, reliability & 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. Bachelor's Degree in Computer Science or related technical field 8+ years of EM experience leading software engineering teams, including experience managing senior engineers, technical leads & managers. Experience leading engineering teams that build and operate large-scale production systems with meaningful reliability, scalability, latency, correctness, availability, security, and operational requirements. Technical depth in one or more of the following areas: 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 one or more of the following: 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 where decisions must be auditable, reproducible, explainable, governed, and secure. Demonstrated ability to hire, grow, coach, and retain strong engineering talent in complex technical areas. Strong communication skills, including the ability to explain technical tradeoffs, risks, execution plans, and platform strategy to engineering, science, product, policy, and business stakeholders. 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 engineering execution, platform architecture, and production outcomes. 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 or managing teams that build 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. Experience managing teams that build model-serving, decisioning, enforcement, workflow, or human-review platforms. Experience with entity intelligence, knowledge graphs, web crawling, domain reputation, business identity resolution, provenance, evidence extraction, or risk scoring. Experience designing or operating human-in-the-loop review systems, appeals workflows, audit platforms, policy reasoning systems, or enforcement governance mechanisms.