Senior Software Engineer, Agentic AI Systems, Google Cloud Security

Write and test product or system development code.Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.Lead the end-to-end design and development of next-gen evaluation system.Research, publish and innovate Cyber Evaluation benchmarks, and develop foundational AI feedback loop with foundational AI model and core cyber harness, agentic system.Minimum qualifications:Bachelor’s degree or equivalent practical experience.5 years of experience with software development in one or more programming languages.3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.2 years of experience with state-of-the art GenAI technologies (e.g., Large Language Model (LLMs), Agentic development etc), applied ML or Core ML development, or ML platform/infrastructure (e.g., model deployment, model evaluation, data processing, debugging).Preferred qualifications:Master's degree or PhD in Computer Science or related technical field.5 years of experience with data structures and algorithms.1 year of experience in a technical leadership role.Experience with RL, post-training, preference optimization or feedback driven model / harness improvement. Experience in highly scalable and available distributed systems, cloud services or data platforms Proven experience in building and deploying Agentic AI systems. End-to-end Agent development and iteration experience, as well as research experience in benchmarking.Understanding of security related areas.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.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.In most instances, this position requires in-person interviews as part of the hiring process.Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.Bachelor’s degree or equivalent practical experience.5 years of experience with software development in one or more programming languages.3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.2 years of experience with state-of-the art GenAI technologies (e.g., Large Language Model (LLMs), Agentic development etc), applied ML or Core ML development, or ML platform/infrastructure (e.g., model deployment, model evaluation, data processing, debugging).