Product Development/DFX Lead - Neuromorphic Computing at Intel

Compensation · Listed in posting

$141,910 – $269,100/yr

Setup: Hybrid
Location: Hillsboro, Oregon · Folsom, California · +3
Type: Full-time
Level: senior
Posted: Sep 1, 2026

Description

Intel is hiring a Senior Product Development Engineer to lead DFT/DFX strategy for its neuromorphic silicon program. The role will architect SoC-level DFT solutions, develop scan-insertion and compression flows, build pre-silicon test-vector frameworks, collaborate with design and circuit experts, and lead silicon power-on, tester content, debugging, and reliability improvements. The position requires extensive DFT experience across architecture through silicon bring-up, including scan, compression, ATPG, IP and SoC-level constructs, and post-silicon debugging. It is an experienced-hire role with hybrid work eligibility and an annual salary range of $141,910 to $269,100 USD across United States locations.

Required Skills

DFT/DFX
Scan insertion
ATPG
System-Verilog
Asynchronous circuits
Post-silicon debugging

Additional Information

Minimum Education: Bachelor's degree with 6+ years of hands-on DFT experience, or a master's degree with 4+ years of hands-on DFT experience from architecture through silicon bring-up.
Required Previous Experiences: 6+ years architecting the full DFT stack, including scan insertion, compression, and ATPG vector generation.
4+ years directly owning IP and SoC-level scan constructs, including JTAG, TAP, and BIST/MBIST.
1 year of post-silicon debugging and driving circuit changes for testability and reliability improvements.
Preferred Qualifications: Experience with custom ASICs or AI/ML accelerator design.
Experience with post-silicon bin splitting, die-level cherry picking, and fault grading.
Strong System-Verilog, System-C, or OOP background with pre-silicon verification experience.
Experience with test-time reduction techniques on automatic test equipment.
Experience with asynchronous architectures and circuits.
1+ year of experience using AI tools for tool-flow setup, debugging, and productivity improvement.