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Job Details
Posted date: Jun 08, 2026
Location: Kirkland, WA
Level: Director
Description
Define, advocate, and execute the overarching Trust and Safety technology roadmap, architecting next-generation AI/ML systems and highly reliable distributed infrastructure to automate and scale global user protection. Oversee the integration of high-availability, low-latency production systems with stringent Service Level Objective (SLO) guarantees, driving excellence across system bottlenecks, data consistency, capacity planning, and cost-efficiency. Steer critical, multi-team technical initiatives from initial abstract discovery through to large-scale deployment, translating high-level business goals into parallelizable engineering workstreams. Define standards for fault-tolerant architectures while mentoring Tech Leads in industry best practices across code quality, CI/CD, comprehensive testing, and systemic technical debt reduction. Partner closely with Product, Policy, and Data Science leadership to co-create the global technology stack, serving as a trusted advisor to executives and abstracting complex technical trade-offs for non-technical stakeholders.Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
In this role, you will steer a high-performing engineering organization. You will architect fault-tolerant, and horizontally scalable solutions across the entire technical stack, ensuring user protection at Google scale.
You will grow in ambiguity, resolving cross-system friction and managing systemic risk across complex distributed networks. You will act as a massive multiplier: defining engineering standards, mentoring tech leads across the organization, enforcing operational excellence and driving cross-functional technical execution.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) + 20% bonus target + bonus + equity + benefits
Learn more about benefits at Google.
Qualifications
Minimum qualifications: Bachelor’s degree or equivalent practical experience.8 years of experience with designing and implementing large-scale distributed systems. 5 years of experience with machine learning (ML) infrastructure or another ML field. Experience with architectural ownership for distributed systems or infrastructure components. Experience building and deploying agentic AI systems.
Preferred qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
Experience managing rapid technical iteration, 0->1 innovation, and managing deep technical ambiguity across multiple engineering organizations. Experience defining organization-wide technical strategies, establishing engineering best practices, and mentoring Executive Engineers and Tech Leads. Background in Trust and Safety, content moderation, security, or anti-abuse engineering at a global scale, managing billions of daily events or real-time streaming data. Strong technical communication skills, with a proven ability to translate complex architectural trade-offs and AI capabilities into recommendations for cross-functional executives.
Extended Qualifications
Bachelor’s degree or equivalent practical experience.8 years of experience with designing and implementing large-scale distributed systems. 5 years of experience with machine learning (ML) infrastructure or another ML field. Experience with architectural ownership for distributed systems or infrastructure components. Experience building and deploying agentic AI systems.