Staff Software Engineer, GenAI Silicon Automation, DeepMind

In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.

In this role, you will shape the future of AI/ML hardware acceleration as a silicon architect/design engineer and drive Tensor Processing Unit (TPU) technology that fuels Google's most demanding AI/ML applications. You will collaborate with hardware and software architects and designers to architect, model, analyze, define and design next-generation TPUs. You will have dynamic, multi-faceted responsibilities in areas such as product definition, design, and implementation, collaborating with the engineering teams to drive the optimal balance between performance, power, features, schedule, and cost. 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 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

• Apply formal methods and constraint programming to the verification of complex computing systems.

• Contribute to open source and internal hardware design flows.

• Lead the transfer of ML-based optimization methods to production-grade tools for hardware engineers.

• Drive the application of formal methods in the loop of ML-based and agentic optimization flows.

• Support an MLIR-based compiler stack.

Qualifications Minimum qualifications:

• Bachelor's degree in Computer Science, related technical field or equivalent practical experience.

• 5 years of experience in hardware design or test.

• 2 years of experience with software development in one or more programming languages.

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related field with a focus on AI/ML.

• 8 years of experience coding in one programming language (e.g., Java, C++, Python, etc.).

• 5 years of experience with machine learning algorithms and tools (e.g., PyTorch, JAX, TensorFlow), artificial intelligence, deep learning, Large Language Model (LLMs), or natural language processing.

• 5 years of experience with data structures and algorithms and hardware-software co-design.

• 3 years of experience in low level ML accelerator programming, compiler or other close to hardware performance programming.