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August 6, 2025

Staff Software Engineer, AI/ML Infrastructure, GCE, GPUs

Senior • On-site

$197,000 - $291,000/yr

Seattle, WA

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 building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture.
  • 3 years of experience with software design and architecture.
  • Experience in GPU programming.

Preferred qualifications:

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • 8 years of experience with data structures/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.

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 help drive innovation in Machine Learning and Artificial Intelligence (AI) by enabling access to hardware accelerators such as Graphical Processing Unit (GPUs) and Tensor Processing Units (TPUs) on Google Cloud. You will enable access to hardware accelerators that power workloads such Large Language Model (LLMs), face recognition and voice processing by ensuring their seamless integration into the software stack through the Google Compute Engine (GCE) accelerators team, making them easily accessible to customers via virtual machines and instances.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge 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.

The US base salary range for this full-time position is $197,000-$291,000 + 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 share 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 benefits at Google.

Responsibilities

  • Collaborate with multiple teams and roles to understand the infrastructure requirements for the new product.
  • Lead efforts across coding, code reviews, quality, reliability, performance, billing and other areas that arise during New Product Introduction (NPI) execution.
  • Drive engineering decisions for the new instance/Virtual Machine (VM) family.
  • Partner with engineers to align and execute effectively.
  • Communicate status updates clearly and align technical decisions across cross-functional teams.