Senior Staff Tech Lead Manager, TPU Workload Onboarding and Optimization

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

Our mission is to provide the best possible cloud-based ML pre-training, post-training, and inference solutions to our customers. The team is part of the AI Engine and focuses on the training/inference workloads and the infrastructure. Our team’s mission is to provide the infrastructure and the framework support to enable serving of ML models on Cloud GPUs and TPUs. In particular, we support external customers for their ML training/inference onboarding and optimizations. You will join a team working on an emergent product with potential to change how customers use Google infrastructure for machine learning training/inference. It is a dynamic and exciting environment that is open to change and is full of growth opportunities.

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.

Responsibilities:

• Act as a Senior Software Manager leading the onboarding for stable stack, and optimization for training and serving solutions on the latest TPU NPIs. Set the technical direction and architect the training/inference serving onboarding and optimization frameworks for our customer needs.
• Collaborate with the ML research, ML performance, model optimization tooling, and other optimization teams.
• Ship stable stack to our customers for training/inference optimization solutions on TPU.
• Prepare and optimize very large reference models, demonstrating state of the art single-host and multi-host inference solutions, especially at large scale.
• Work closely with customers to onboard their workloads to production and optimization.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience with software development.
  
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 2 years of experience with state of the art training (e.g. Megatron-LM, DeepSpeed) and inference techniques (e.g. TensorRT-LLM, vLLM, SGLang).
• 2 years of experience in a people management or team leadership role.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• Experience in optimizing machine learning models for large scale training/inference workloads.
• Experience in different large scale ML optimizations techniques for improving latency and throughput.
• Experience with accelerators (TPUs or GPUs), or HPC .