Senior ASIC DFT Engineer (AI Hardware)

TL;DR

Senior ASIC DFT Engineer (AI Hardware): Defining and implementing DFT architecture for next-generation AI acceleration chips with an accent on scan, compression, JTAG, MBIST, ATPG, and fault coverage closure. Focus on architecting complex test solutions, advancing AI-assisted DFT workflows, and supporting silicon bring-up, debug, and yield analysis.

Location: Hybrid work model in Haifa, Israel, with time split between the assigned hirify.global site and off-site work.

Company

hirify.global is developing next-generation AI hardware solutions and semiconductor products.

What you will do

• Define DFT architecture and strategy for complex AI acceleration IPs and subsystems, balancing coverage, area, timing, and power.
• Implement scan, compression, boundary scan through JTAG, MBIST, and test access and control structures.
• Lead ATPG and pattern generation through fault coverage closure and sign-off.
• Develop DFT methodologies, workflows, and best practices for a newly established team.
• Advance the effective use of AI tools throughout the DFT flow.
• Collaborate with design, verification, physical design, and product/test engineering teams during silicon bring-up, debug, and yield analysis.

Requirements

• Bachelor's degree in Electrical Engineering, Computer Engineering, or an equivalent field.
• 6+ years of hands-on DFT experience, including ownership from architecture through silicon bring-up.
• Deep expertise in scan insertion, compression, ATPG, boundary scan/JTAG, and MBIST.
• Experience driving complex technical decisions across cross-functional engineering teams.
• Strong interest in applying AI tools to improve DFT workflows.

Nice to have

• DFT experience with large, high-performance, or AI/ML accelerator designs.
• Experience with hierarchical DFT, low-power test, in-system test, or silicon debug and bring-up.
• Familiarity with design, verification, and physical design processes.

Culture & Benefits

• Startup-like culture within hirify.global with fast execution and short feedback loops.
• Broad technical influence and significant ownership in a team being built from scratch.
• Hybrid work model combining on-site and off-site work.
• Active adoption of AI across architecture, design, verification, and development workflows.