Staff Applications Engineer - DFT & AI-Driven Test Solutions

Synopsys Inc; Warszawa; On-site; PLN 300,000 - 420,000; Full time

Job summary

Synopsys Inc in Warsaw invites an experienced DFT engineer to join our Test Applications Engineering team. You will design, validate, and optimize advanced test solutions for digital IC designs, collaborating with customers to deliver robust test flows and bring-up strategies.

We expect a strong background in RTL coding, DFT insertion, ATPG, MBIST architecture, and familiarity with agentic AI-driven tooling.

Qualifications

• Experience in RTL coding, DFT insertion, scan synthesis, ATPG, and MBIST architecture planning.
• Strong pattern generation and silicon bring-up knowledge.
• Fluent in communicating complex technical concepts to customers.

Responsibilities

• Provide expertise for test solutions during design planning, budgeting, and implementation.
• Design rules checking for test methodologies during RTL coding stages.
• Implement and validate DFT techniques such as scan compression, core wrapping, and BIST.
• Generate and validate ATPG patterns with advanced fault models.
• Participate in customer design and flow reviews for seamless integration.
• Analyze and debug diagnostics to resolve issues efficiently.
• Support tape-out readiness with high-quality test logic and patterns.
• Deploy agentic AI flows for DFT at customer sites and feed findings to product roadmap.

Skills

DFT methodologies

Digital IC design

RTL coding

ATPG

MBIST architecture

Education

BSc in Electrical Engineering

MSc in Electrical Engineering

Tools

MBIST tooling

ATPG patterns

LLM-assisted tooling

Job description

Synopsys Inc in Warsaw invites an experienced DFT engineer to join our Test Applications Engineering team. You will design, validate, and optimize advanced test solutions for digital IC designs, collaborating with customers to deliver robust test flows and bring-up strategies.

We expect a strong background in RTL coding, DFT insertion, ATPG, MBIST architecture, and familiarity with agentic AI-driven tooling.