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.
Act as ML software engineer 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 onboarding and optimization frameworks for our customer needs. Work closely with customers to onboard their workloads to production and optimization.
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.
Minimum qualifications
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
4 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).
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.
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
4 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).
