Senior Engineering Manager AI Inference Platform, Distributed Cloud

In this role, you will be pivotal in architecting and optimizing the serving stack for models like Gemini in an on-prem cloud environment, addressing exciting challenges to improve speed and efficiency. This is a unique opportunity to go deep, leading system-level design and performance profiling, ensuring Google's LLMs run faster and more cost-effectively than ever before.

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.

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

US: $262000 - $365000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience programming in C++ or Python.
• 7 years of experience optimizing, profiling, and scaling production-grade systems on GPU accelerators or specialized AI hardware.
• 5 years of experience directly managing and leading engineering teams focused on machine learning infrastructure, AI platforms, or high-performance distributed computing systems.
• 5 years of experience in a people management or team leadership role.
• 4 years of experience managing engineering organizations across multi-team infrastructure dependencies.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 5 years of experience working in a complex, matrixed organization.
• 5 years of experience implementing advanced LLM serving architectures and optimization techniques, such as disaggregated serving, continuous batching, or specialized compiler technologies (e.g., XLA).
• 4 years of experience utilizing deep-dive ML profiling tools (e.g., Nsight, xprof) to troubleshoot and resolve low-level bottlenecks within major frameworks like JAX, PyTorch, or TensorFlow.