Software Engineer, Machine Learning, TPU Workload Optimization

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. Our mission is to provide the best possible cloud-based ML pre-training, post-training, and inference solutions to our customers. This 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. We work on an emergent product with the potential to change how customers use Google infrastructure for machine learning training/inference. 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 team 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. Possess extensive technical knowledge of firmware design, kernel development, and server system integration, with the ability to influence the ecosystem. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality. Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies. Lead the server software design, development, and implementation on a scalable and high-quality Cloud Server Program, overseeing the project from conception to launch.

Minimum qualifications: Bachelor’s degree or equivalent practical experience. 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree. 1 year of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 1 year of experience with training (e.g., Megatron-LM, DeepSpeed) and inference techniques (e.g., TensorRT-LLM, vLLM, SGLang). Preferred qualifications: Master's degree or PhD in Computer Science or related technical fields. 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