Staff Software Engineer, Data Center Systems Lead

Socket.dev; Sunnyvale (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google is seeking a senior lead software engineer to join the AI and Infrastructure team, focusing on the next-generation data center resource modeling platform. You will guide architecture, set roadmap, and deliver scalable hardware-software integration across the Google fleet.

You will collaborate with teams to plan capacity, monitor data center hardware, and manage machine life-cycle at scale, while reducing total cost of operations and driving innovation.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in programming.
• 5 years testing and launching software products.
• 5 years building 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 with distributed computing, large-scale data processing.

Responsibilities

• Provide technical leadership on high-impact projects.
• Influence and coach a distributed team of engineers.
• Facilitate alignment and clarity across teams on goals, outcomes, and timelines.
• Assess impact to team software stack from new hardware product programs and explore AI driven process improvement and automation.
• Establish technology and product goals in collaboration with other tech leads, managers, partners, clients, and executive leadership. Grow technical skills within the team.

Skills

Programming experience

Distributed computing

Large-scale data processing

Software design & architecture

Education

Bachelor's degree or equivalent

Job description

Google is seeking a senior lead software engineer to join the AI and Infrastructure team, focusing on the next-generation data center resource modeling platform. You will guide architecture, set roadmap, and deliver scalable hardware-software integration across the Google fleet.

You will collaborate with teams to plan capacity, monitor data center hardware, and manage machine life-cycle at scale, while reducing total cost of operations and driving innovation.