Join Google Cloud and shape the future of AI/ML hardware acceleration. This role offers the chance to drive the Tensor Processing Unit (TPU) technology powering Google's advanced AI/ML applications.

You will be part of a forward-thinking team developing custom silicon solutions that define the next generation of Google's TPUs. Contribute to innovations behind globally recognized products by leveraging your design and verification expertise.

Focus on verifying intricate digital designs, particularly the TPU architecture and its integration within AI/ML-driven systems. The AI and Infrastructure team is dedicated to pushing the boundaries of what's possible, delivering AI and Infrastructure at unparalleled scale, efficiency, and reliability.

Take ownership of the complete verification lifecycle, from planning and test execution to coverage closure. You will focus on meeting stringent AI/ML performance and accuracy goals.

Develop constrained-random verification environments capable of uncovering corner-case bugs. Ensure the reliability of AI/ML workloads on Tensor Processing Unit (TPU) hardware.

Collaborate closely with design and verification engineers on active projects. Plan verification for digital design blocks and identify critical verification scenarios.

Define and implement all types of coverage measures for stimulus and corner-cases. Debug tests with design engineers to deliver functionally correct design blocks.

Requires a Bachelor's degree in Electrical Engineering or equivalent practical experience, alongside a minimum of 4 years of dedicated experience in verification.

This includes verifying digital logic at the RTL level using SystemVerilog or Specman/E for FPGAs or ASICs. Experience verifying digital systems with standard IP components and interconnects (e.g., microprocessor cores, hierarchical memory subsystems) is essential.

Proven experience in the verification and debug of IP/subsystem/SoCs within the networking domain, covering aspects like packet processing, bandwidth management, and congestion control, is expected.

Preferred qualifications include a Master's degree in Electrical Engineering or a related field, along with experience in AI/ML accelerators or vector processing units.