Senior Software Engineer, Gemini Deployment and Performance, DeepMind

Google•Mountain View, CA

1d•$262,000 - $364,000

About The Position

The Senior Software Engineer, Gemini Deployment and Performance role focuses on scaling Gemini serving to billions of daily requests and millions of developers. The position provides technical leadership for production infrastructure and performance improvements, and directs inference and efficiency strategies to maintain competitive performance as state-of-the-art technology evolves. The role includes mentoring and developing engineers, fostering technical excellence, collaboration, high agency, and an AI-first mindset. It partners across Research, Inference Engine, Model Server, Serving, Reliability, and Cloud AI teams to navigate cross-stack dependencies and bring state-of-the-art techniques into production. As a technical authority on the serving stack, the role drives scalability and automates validation pipelines to shorten the path from checkpoint delivery to production release. The position is part of Google DeepMind, an interdisciplinary AI lab focused on advancing AI to address complex global challenges and enable product innovation. The posting describes leadership across engineering teams and large-scale projects. Individual pay is determined by job-related skills, experience, and relevant education or training. The stated U.S. compensation is $262,000–$364,000, plus a 25% bonus target, equity, and benefits.

Requirements

Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience in a technical leadership role overseeing projects.
3 years of experience in a people management, supervision, or team leadership role.

Nice To Haves

Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
5 years of experience working in a complex, matrixed organization.

Responsibilities

Scale Gemini serving to billions of daily requests and millions of developers by landing infrastructure and tuning improvements in production endpoints to meet company year-over-year TPS and chip goals.
Direct inference and efficiency strategies, sustaining Pareto-optimal performance against rapidly evolving state-of-the-art technology.
Mentor and grow engineers while cultivating technical excellence, high agency, collaboration, and an AI-first mindset.
Partner across Research, Inference Engine, Model Server, Serving, Reliability, and Cloud AI, navigating cross-stack dependencies to accelerate the deployment of state-of-the-art techniques.
Serve as a technical authority on the serving stack, drive high scalability, and automate validation pipelines to accelerate the transition from checkpoint delivery to production release.