NVIDIA is seeking a highly motivated DFT Engineer to join our silicon engineering team. In this role, you will work closely with Design, Test, CAD, and Silicon Engineering teams to validate and debug Design-for-Test (DFT) features in some of the industry's most advanced GPUs and SoCs.
This position offers an excellent opportunity to learn industry-leading DFT methodologies and contribute to cutting-edge products powering AI, accelerated computing, and next-generation platforms.
What you'll be doing
- Verify DFT features and test infrastructures, including Scan, MBIST, JTAG, and related functionality.
- Collaborate with Design, Integration, CAD, and Test Engineering teams to define verification scope and test strategies.
- Execute verification and validation activities at the IP, subsystem, and full-chip levels.
- Perform RTL and gate-level debugging to identify and resolve design and test issues.
- Generate and validate test patterns and support silicon bring-up and debug activities.
- Improve engineering productivity through scripting, automation, and workflow enhancements.
- Support verification flow and methodology improvements.
What we need to see
- 1+ years of experience with an M.S. degree in Electrical Engineering, Electronics Engineering, Microelectronics, IC Design, or a closely related discipline.
- Strong understanding of digital circuits, computer architecture, and semiconductor design fundamentals.
- Strong analytical, problem-solving, and debugging skills.
- Familiarity with RTL simulation and debug tools such as Verdi and VCS.
- Basic understanding of SoC architecture, including clocking, reset, power, and I/O concepts.
- Programming or scripting experience with Python, Tcl, Perl, C, or C++.
- Fluent verbal and written communication skills in English.
- Strong teamwork skills and willingness to learn in a fast-paced environment.
Ways to stand out from the crowd
- Understanding of DFT methodologies, including Scan, ATPG, MBIST, JTAG, and Boundary Scan.
- Experience with RTL or gate-level simulation and debugging.
- Familiarity with silicon bring-up, hardware validation, or failure analysis.
- Experience developing automation tools and engineering workflows.
- Exposure to AI-assisted engineering tools, machine learning, or data analytics.