Staff Software Engineer, Generative AI, Core Machine Learning

corporate_fareGoogleplaceMountain View, CA, USA

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential).
• 5 years of experience testing, and launching software products.
• 1 year of experience in building Generative AI or agentic applications.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a ML-related technical field.
• 8 years of experience with data structures and algorithms.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Production experience in applying ML related features to a scalable system.

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.

In this role, you will transform the development of AI agents from an artisanal craft into an engineering discipline to achieve high-quality, reliability with multi-folds efficiency gains across Google. You will architect and productionize horizontal infrastructure, including generalized Knowledge Store libraries and self-reflection modules. You will apply core design principles, lead deep audits of high-impact agents to extract deterministic logic from monolithic prompts into efficient, code-based workflows. You will scale automated prompt optimization and trace scanning tools to systematically eliminate waste and solve the "GenAI engineering gap" for teams building production-ready agents. Your work will bridge the gap between experimental prototypes and scalable production infrastructure.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.

Responsibilities

• Generalize horizontal tools, including a plug-in Knowledge Store and a Self-Reflection module, into reusable services that any agent can adopt to enable automated learning and improvement.
• Apply validated design principles to transition deterministic logic from monolithic prompts into efficient, code-based workflows that maximize reasoning value and ensure precise context control.
• Execute deep architectural reviews and automated trace scanning for high-impact agents to eliminate structural inefficiencies and redundant tool calls in multi-turn reasoning chains.
• Extend automated prompt optimization frameworks to support multi-step workflows, enabling systematic improvement of both efficiency and quality across the agent ecosystem.
• Implement code-level safety enforcements and structured tool return patterns within the agent platform to ensure reliability and correctness on deterministic subtasks.