Staff Software Engineer, AI/ML Infrastructure, TPU Supercomputers

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

Benefits offered by this job

Equity

Bonus target 20%

Benefits package

Job summary

Google's Emergent AI infrastructure team is building the next generation of on-prem AI infrastructure to empower Frontier model and AI solution builders. We are redefining what's possible and delivering AI at scale, with billions of users and Google Cloud customers in scope.

As a software engineer, you will lead end-to-end delivery, architect secure boot flows, and collaborate with hardware teams to push the performance and reliability boundaries of distributed systems.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of software development in C, C++, Go, or Python.
• 5 years testing and launching software products.
• 5 years building large-scale infrastructure, distributed systems or networks.
• 3 years designing, building and operating large-scale distributed systems.

Responsibilities

• Lead the design and end-to-end software delivery of specialized AI compute platforms, ensuring high availability for massive-scale workloads.
• Establish observability and hardening strategies for the lower-half software stack, including firmware, and hardware qualification.
• Architect robust integration interfaces between custom compute topologies and industry-standard workload schedulers like Kubernetes and GKE.
• Architect secure boot and cryptographic remote attestation flows for distributed hardware platforms.
• Partner closely with hardware engineering and chip design teams to influence future architectures for large-scale deployment.

Skills

Software development

Testing & release experience

Large-scale infrastructure

Distributed systems design

Data structures / algorithms

Technical leadership

Cross-functional collaboration

Security implementations

Kubernetes / GKE

Education

Bachelor's degree or equivalent practical experience

Master’s degree or PhD in Engineering / CS

Job description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of software development experience in C, C++, Go, or Python.
• 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 designing, building, and operating large-scale distributed systems, high-performance networking stacks, or operating system internals.

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.
• Experience with lower-half server architectures, hardware-adjacent orchestration, and low-level security implementations.
• Experience with Kubernetes and Google-internal cluster systems, alongside a proven ability to build telemetry pipelines and monitoring systems for distributed hardware.

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.

The Emergent AI infrastructure team in Google is looking to build the next generation of on-prem AI infrastructure to bring the best of Google to empower Frontier model and AI solution builders to advance AI around the world.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Lead the design and end-to-end software delivery of specialized AI compute platforms, ensuring high availability for massive-scale workloads.
• Establish observability and hardening strategies for the lower-half software stack, including firmware, and hardware qualification.
• Architect robust integration interfaces between custom compute topologies and industry-standard workload schedulers like Kubernetes and GKE.
• Architect secure boot and cryptographic remote attestation flows for distributed hardware platforms.
• Partner closely with hardware engineering and chip design teams to influence future architectures for large-scale deployment.