Senior Staff Software Engineer, GCS AI/ML Onboarding

Minimum Qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• Experience with capacity planning.
• Experience with distributed systems.
• Experience with cross functional leadership

Preferred Qualifications:

• 5 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.
• Experience mentoring executive engineers and other Tech Leads.
• Understanding of AI/ML frameworks and the ability to navigate ambiguity, lead technical in-depth search into complex problems, evaluate trade-offs, and drive consensus among senior engineers and stakeholders.
• Excellent communication and collaboration skills to influence across organizational boundaries.

Experience with distributed systems.Experience with distributed systems.

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.

In this role, you will have deep expertise in large-scale capacity planning, distributed systems, networking, and AI/ML frameworks. You will have a proven track record of setting tactical direction and influencing multiple teams and their line team leads (TLs), both within and outside their direct organization. You will be accountable for a business-critical domain, operating with the excellent judgment and minimal oversight expected of this level. You will be ensuring Google Cloud Storage (GCS) continues to be the storage for the next generation of AI/ML workloads on Google Cloud Platform.

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 $248,000-$349,000 + 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

• Develop and implement a comprehensive capacity planning strategy for AI/ML workloads. Create models for our storage, serving, and networking capacity, and defining our risk models and utilization goals across the fleet.
• Provide technical leadership and set the tactical direction for several engineering teams and their respective Tech Leads, ensuring a unified and effective approach.
• Partner with various teams, including those responsible for data center management, central capacity planning and tooling, as well as our partners in Compute and Networking. 
• Address the novel issues presented by the exponential growth in throughput demand from new AI/ML workloads. 
• Take ownership of and drive improvements for key metrics like the time required to onboard new high-throughput customers and the unit cost per byte stored and throughput served.