Staff Software Engineer, Infrastructure, Platforms Infrastructure Engineering

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.

Preferred Qualifications:

• 10 years of experience in software development.
• 5 years of experience in a technical leadership role.
• Experience in building the infrastructure ecosystem that supports data center modeling and deployments.
• Experience working with AI agent-based applications.
• Ability to engage with other teams.
• Ability to work in a fast-paced environment.

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.

As a part of the Staff Software Engineering Technical Lead role (TL), you will lead the development of our next-generation NPI (New Product Introduction) onboarding process and workflow automation for managing our extensive fleet and integrating ML technologies. You will lead an engineering team building a data center resource platform that enables Google-wide fleet onboarding, capacity planning, and machine life-cycle management at scale while minimizing operational costs.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

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.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

• Lead, mentor, coach, and grow engineering team, fostering team cohesion.
• Establish the technology and product goal in collaboration with technical leads, partners, clients, and executive leadership.
• Drive project planning, prioritization, and resource allocation to meet both long-term and short-term goals.
• Establish engineering best practices in collaboration with the broader engineering team.
• Collaborate with PM, UX, TPM, and stakeholders across the PIE organization and Google.