Minimum qualifications: Bachelor's degree or equivalent practical experience. 8 years of experience with software development, system design, or machine learning infrastructure. 8 years of experience working with trust and safety, classification problems, large language model (LLM) safety architectures, fraud or risk detection, identity verification, or adversarial machine learning. Preferred qualifications: Familiarity with industrial-scale data pipelines, internal data orchestration engines, and high-performance compute infrastructure. Demonstrated ability to lead by influence and drive technical alignment across sprawling, matrixed engineering groups without direct operational authority. Exceptional systems-thinking capabilities, with a documented history of transforming fragmented technical processes into highly reusable, automated platform engines. About the job 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. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. As a part of the GenAI for Youth, a team within Kids and Families, you will be dedicated to developing the next-generation of scalable, systemically responsible, and high-integrity AI frameworks that power experiences across Google's products. Your mission is to bridge the gap between foundational model research and highly calibrated, age-appropriate production environments. You will build unified infrastructure that solves complex engineering, user classification, and behavioral modeling challenges once for the entire ecosystem. You will operate across multi-site engineering hubs, establish the technical strategy, algorithmic steering frameworks, and automated validation layers necessary to ensure that Google's AI-driven platform surfaces scale with complete structural accuracy, performance safety, and long-term ecosystem health.The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Develop shared algorithmic frameworks and foundational math in core modeling domains, including runtime behavior steering, latent-space pattern detection, optimization loops, and fine-tuning rooted in industry best practices. Streamline engineering efforts across global product lines by shifting fragmented, application-specific pipelines into cohesive, highly scalable, and reusable platform layers. Architect definitive, automated evaluation methodologies and standardized benchmarking metrics to provide scientifically rigorous validation models, eliminating manual execution overhead. Serve as a technical conduit between product, data science, applied engineering groups, machine learning research organizations, and centralized core infrastructure teams. Act as a strategic advisory partner to executive engineering leadership on platform maturity, long-term technical debt reduction, and operational capacity across technical workstreams.