Lead DFT Engineer

EnCharge AI; United States; On-site; USD 200,000 - 250,000; Full time

Job summary

EnCharge AI in the United States is seeking a Design for Test (DFT) Lead to architect end-to-end DFT for complex SoCs, including Boundary Scan and MBIST, with focus on testability for edge-to-cloud hardware.

You will drive IST and POST strategies for in-systems health, oversee ATPG and scan insertion, and partner with Design, Physical Design, and Yield teams to balance coverage with area and power.

Qualifications

• 12+ years in DFT with leadership experience
• Bachelor’s degree in a related field
• Expertise with MBIST and boundary scan technologies
• Experience with ATPG for stuck-at, path delay, transitions
• Proficiency with MBIST repair and IJTAG (IEEE 1687)
• Experience with FinFET nodes (7nm+)
• Experience in low-power DFT for multi-voltage designs

Responsibilities

• Architect end-to-end DFT architecture for complex SoCs, including Boundary Scan and MBIST
• Develop IST and POST strategies for edge data centers
• Oversee scan insertion and ATPG (stuck-at, transition, path delay)
• Lead Memory/Logic BIST initiatives and repair capabilities
• Collaborate with Design, Physical Design, Yield to balance coverage vs. area/power
• Lead post-silicon bring-up and debug on ATE to minimize test time

Skills

DFT leadership

MBIST

Boundary Scan

IJTAG

ATPG

Memory/Logic Test

Power-aware testing

Edge data-center test

Education

Bachelor’s degree in a related field

Tools

Synopsys TestMAX

Mentor Tessent

Cadence Modus

Job description

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

Job Description:

Developing silicon for edge-to-cloud computing isn't just about speed; it’s about balancing high-performance data processing with extreme power efficiency and reliability in remote environments.

As the Design for Test (DFT) Lead, you will be the architect of our testing strategy, ensuring our data center chips are flawlessly manufacturable and resilient enough for edge deployment.

Key Responsibilities:

Architectural Leadership: Define and implement the end-to-end DFT architecture for complex SoCs, including Hierarchical DFT, Scan compression, Boundary Scan and MBIST.

Edge-Specific Reliability: Develop strategies for In-System Test (IST) and power-on self-test (POST) to ensure chip health in remote edge data centers.

Implementation & Flow: Oversee scan insertion, ATPG (Stuck-at, Transition, Path Delay), and

Memory/Logic BIST.

Cross-Functional Synergy: Collaborate with Design, Physical Design, and Yield teams to ensure high test coverage while minimizing area overhead and power impact as well as timing analysis.

Post-Silicon Validation: Lead the bring-up and debug phase on ATE (Automated Test Equipment) to root-cause silicon failures and optimize test time.

Technical Requirements:

Experience: 12+ years in DFT, with at least 2 years in a leadership or principal role. Bachelor’s degree in a related field.

Tools: Mastery of industry-standard tools (e.g., Synopsys TestMAX, Siemens/Mentor Tessent, Cadence Modus).

Memory & Logic Test: Deep expertise in MBIST (Memory Built-In Self-Test) with repair capabilities, SCAN, IJTAG (IEEE 1687) and boundary scan (IEEE 1149.1/6).

Advanced Nodes: Proven track record with FinFET nodes (7nm, 5nm, or below).

Low Power: Experience managing DFT in multi-voltage/power-gated designs—crucial for edge efficiency.

EnCharge AI is an equal employment opportunity employer in the United States.

The salary range for this position is $200,000 to $250,000 USD/CAN per year. Actual compensation offered will be determined based on job-related knowledge, skills, and experience.