Shape the future of AI/ML hardware acceleration by driving the development of Tensor Processing Units (TPUs) that power Google's most demanding AI/ML applications. You will be instrumental in developing custom silicon solutions and contributing to innovations behind products used by millions worldwide.

This role offers a unique opportunity to leverage your design and verification expertise to ensure the reliability of complex digital designs, with a specific focus on TPU architecture and its seamless integration within AI/ML-driven systems. Join a forward-thinking team that redefines possibilities in AI and Infrastructure, empowering Google Cloud customers with breakthrough capabilities at unparalleled scale.

Own the complete verification lifecycle, from planning and test execution to coverage closure, with a strong emphasis on meeting stringent AI/ML performance and accuracy goals.

Build robust, constrained-random verification environments using SystemVerilog and UVM to uncover corner-case bugs and guarantee the reliability of AI/ML workloads on TPU hardware.

Collaborate closely with design and verification engineers, plan digital design block verification, and identify critical verification scenarios.

Define and implement comprehensive coverage measures for stimulus and corner cases, debug tests with design engineers, and measure verification progress towards tape-out.

A Bachelor's degree in Electrical Engineering or equivalent practical experience is required.

Four years of experience in verification, specifically with digital logic at RTL level using SystemVerilog or Specman/E for FPGAs or ASICs.

Demonstrated experience verifying digital systems with standard IP components and interconnects, such as microprocessor cores and hierarchical memory subsystems.

Proficiency in verification and debugging of IP, subsystem, or SoCs within the networking domain, including areas like packet processing, bandwidth management, and congestion control.

Experience with an industry-standard simulator, revision control systems, and regression systems is preferred.

Familiarity with Artificial Intelligence/Machine Learning (AI/ML) accelerators or vector processing units, along with full verification lifecycle experience, are highly desirable.

Excellent problem-solving and communication skills are essential.