AI Deployment Engineer, Organizational Engineer, DeepMind

AI Deployment Engineer, Organizational Engineer, DeepMind corporate_fare DeepMind place Mountain View, CA, USA Minimum qualifications: Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience. 2 years of experience in software development, including designing and building full-stack web applications. Experience with general-purpose programming languages (e.g., Python, TypeScript, Java, or Go). Experience with software engineering, including data structures, algorithms, and testing. Ability to work with teams in different time zones, specifically Mountain View and London.

Preferred qualifications:

Experience with front-end frameworks (e.g., Angular, React, or similar) for building intuitive interfaces. Experience with modern AI application development, including techniques like Retrieval-Augmented Generation (RAG), agentic workflows, function calling, structured decoding. Familiarity with Google or Google DeepMind internal development tools and infrastructure. Ability to work with a high degree of autonomy in a changing ambiguous environment and deliver impactful results. Track record of building and shipping high-quality software, from early-stage prototypes to maintained production systems. A product-oriented and full-stack mindset—driven by user needs, comfortable working across different parts of the stack, and attentive to good user experience. About the job We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Responsibilities Design, develop, and maintain internal tools and AI-powered platforms that automate work and increase productivity across DeepMind. Take full-stack ownership of projects from initial ideation and architecture through building, testing, deployment, and user adoption. Collaborate closely with teams across DeepMind from research to operations to understand their workflows, identify opportunities for AI-powered improvement, and deliver solutions that are adopted and valued. Build and iterate rapidly on AI-enabled utilities and automations, moving them from concept to production with speed and quality. Stay current on the latest AI models, tools, and engineering patterns, and apply that knowledge to ship better solutions.