Staff Software Engineer, GeminiApp Personalization, DeepMind

Staff Software Engineer, GeminiApp Personalization, DeepMind corporate_fare DeepMind place Mountain View, CA, USA Minimum qualifications: Bachelor's degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience testing, and launching software products. 5 years of experience working with big data analytics, machine learning, AI, large language models (LLM) to process and create data. 5 years of experience in data analysis or data science, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data. 3 years of experience with software design and architecture.

Preferred qualifications:

Master's degree or PhD in Engineering, Computer Science, or a related technical field. 8 years of experience with data structures and algorithms. 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. 3 years of experience in a technical leadership role leading project teams and setting technical direction. Experience with AI/ML techniques including recommender systems, generative AI, large language models, information retrieval, etc. About the job At Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Software Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team. We are the Gemini App Personalization team at Google DeepMind, dedicated to building Google's next-generation AI assistant. Our mission is to empower billions of people by offering deeply personalized products that act as a seamless extension of their own intellect. In this role, you will help build a personal AI assistant that continuously absorbs, organizes, and effortlessly recalls the unique interests, passions, and curiosities of individuals, evolving alongside them to amplify their everyday thinking. 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. The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . Responsibilities Design, prototype, and build scalable features on the full Gemini App stack that securely capture, organize, and intuitively surface long-term personal context. Perform data analysis of user feedback, logs, and evaluation tasks to identify opportunities for improving how effectively the assistant retains and synthesizes historical user interactions. Develop evaluation techniques (both automated and human-in-the-loop) to assess and hill-climb on the quality of contextual recall and the assistant's ability to act as a seamless cognitive extension. Act as the primary owner of model output quality, ensuring responses accurately reflect, synthesize, and build upon the user's unique history and ongoing preferences. Contribute to the development of a data flywheel that safely accumulates and leverages continuous user context, driving ongoing improvement and innovation.