• As part of Operations Engineering team that plays a key role in developing new GPU products, you will architect, design, and implement efficient manufacturing test processes for our state of the art GPU chips
• Identify and implement test processes to improve manufacturing efficiency for our latest GPU architectures, built in leading-edge process technology
• Analyze product yield limiters and drive resolution of failures on Automatic Test Equipment (ATE) while leading multi-functional efforts with design, foundry, quality, test to root-cause problems and implement improvements
• Define product test requirements and drive NPI bring up, validation, and characterization for smooth production ramp at worldwide manufacturing sites as you continuously find opportunities for potential process improvements and collaborate with multi-functional teams to implement
• Influence and optimize the accurate placement of test coverage across our test and supply chain to reduce cost and production waste. Lead detailed data analysis methods to correlate customer failures with internal production data to help understand root cause, risk, and exposure

• Bachelor or above degree in Electrical Engineering/Computer Engineering /Physics with 2+ years of experience.
• Experience in post-silicon characterization, correlation, qualification and debug.
• Knowledgeable in the area of ATE testing, DFT methodology, cmos device physics.
• Strength presenting & communicate to technical audiences and methodical problem-solving with critical thinking, solution focused and attention to detail.
• Highly motivated with strong sense of ownership, have excellent interpersonal skills, and high sense of team-work with intellectual honesty.

• Experience with DRAM testing and familiarity with 2.5D assembly.
• Understanding of DFT insertion techniques including SCAN, ATPG, MBIST and IOBIST.
• Familiarity with O+/JMP related data analytic tools, python/java or any other scripting language
• Experience with ATE test program, test system & hardware such as 93K, uflex etc. for Silicon bring-up and debug, as well as utilizing Failure Diagnosis/Isolation tools to root cause fix Silicon manufacturing defects
• Passionate about experimental work and data evaluation, hunger to get to the bottom of understanding where models fall short in the real world.