Senior Application Engineer - Semiconductor Test

The Role: This role places you near one of the most important semiconductor buildouts underway: AI compute and inference. You will help bring up, validate, and optimize advanced SoC devices for high-performance computing and large-scale AI infrastructure-working across silicon, software, ATE, and customer programs. We are seeking a hands-on Semiconductor Test Application Engineer in San Jose, CA. This role is ideal for an experienced ATE, validation, test development, or system-level debug engineer who wants to solve complex semiconductor test challenges with lab hardware, customer teams, and next-generation Advantest V93000 platforms. You will become a key technical owner for Advantest SmarTest 8 and V93000-based SoC test solutions, helping architect, debug, and optimize production-grade test programs for complex devices. Because the work includes hardware bring-up, lab execution, customer collaboration, and fast-moving ramps, the position is based on-site. What You Will DoTest Program Development: Architect, write, debug, and optimize SoC test programs in modern ATE software environments, including Advantest SmarTest 8, for advanced AI compute, inference, and high-performance semiconductor devices. Silicon Bring-Up and Validation: Support and help drive first-silicon bring-up on V93000 testers, debugging interactions between silicon, system behavior, test content, and the tester environment. Pattern Workflow Engineering: Drive pattern translation, validation, and debug across DFT and tester workflows. Work with STIL, WGL, or comparable pattern sources; support tester bring-up; and collaborate with design and product engineering on emerging virtual test workflows using SmarTest 8 and V93000 environments. Hardware Co-Design and Debug: Collaborate with PCB and hardware teams to specify, validate, and troubleshoot high-density Device Interface Boards (DIBs), including signal integrity, power delivery, and tester-to-device interfaces. Test Time and Automation: Build automation in Python, Java, C++, or similar languages to analyze logs and STDF data, streamline debug, interface with test platforms, improve multi-site efficiency, and accelerate learning loops.