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.

Google Cloud is searching for a highly skilled and motivated engineer to optimize machine learning model performance for our customers and help them achieve maximum model performance for large scale training and inference through tuning and optimization at both software and hardware levels. In this role, you will collaborate closely with customers, write custom kernels, and develop custom solutions to meet their unique model performance requirements. A deep understanding of deep learning frameworks (like PyTorch or JAX), strong coding skills, excellent communication abilities, and a passion for mentoring junior engineers are essential for success in this role.

Responsibilities

• Optimize ML model architectures and systems for high performance across multiple TPU platforms, including onboard hardware and simulation environments.
• Enhance model and system performance for both low-latency inference and large-scale distributed training workloads.
• Develop post-training algorithms, such as quantization and low-level kernel optimizations, to increase inference speed and reduce memory consumption on modern GPU and TPU architectures.
• Engineer custom kernels to maximize training efficiency for memory-bound large models and I/O-bound fine-tuning processes.
• Collaborate with ML infrastructure teams, hardware and simulation departments, and Alphabet’s research teams to integrate cross-functional optimizations.