EDA Workflow Optimization Engineer

at Nvidia

USD 152,000-287,500 per year

MIDDLE SENIOR

✅ Hybrid

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Agile Communication @ 3 Data Analysis @ 5 Debugging @ 6 Distributed Systems @ 6 GPU @ 6 Networking @ 3 Perl @ 6 Python @ 3 Slurm @ 3

Details

NVIDIA's EDA Workflow Optimization team partners with engineering teams worldwide to understand workflows spanning the full chip design process, from inception through study, architecture, design, verification, emulation, layout, packaging, power-on, and production. The team guides improvements and re-invents workflows, builds infrastructure, investigates flaws and opportunities, constructs performance metrics, and develops scalable, reliable, high-performance systems and tools in an agile production software environment.

Responsibilities

Perform fast-paced investigations to empower engineers to develop efficiently.
Participate in the full life cycle of tool development, testing, and deployment.
Work with team members and chip engineers to understand and optimize workflow usage of compute and storage environments.
Build reliable, easy-to-use metrics for hundreds of engineers worldwide.
Continuously improve the chip development process.
Contribute to the quality and time to market of next-generation chips.

Requirements

Strong experience investigating and debugging complex, multidisciplinary problems in a UNIX engineering environment.
Hands-on experience making architectural decisions involving storage, networking, and compute technologies.
Experience with ASIC, VLSI, CAD/EDA, or mixed-signal design workflow environments.
Hands-on experience with EDA tools.
Experience with UNIX systems programming and automation using Python and/or Shell.
Authoritative-level knowledge of UNIX and UNIX utilities.
Excellent planning and communication skills.
Experience applying data analysis principles and influencing data-driven decisions.
Flexibility and adaptability in a dynamic environment with changing requirements.
Bachelor's degree in Computer Science or equivalent experience; a master's degree is preferred.
Five or more years of relevant experience.

Preferred Qualifications

Experience with job schedulers, particularly IBM Spectrum LSF and/or SLURM.
Experience running GPU-based workloads in batch computing environments and a deep understanding of distributed systems principles.
Strong programming and debugging skills with C/C++, Python, and Perl on UNIX.
Passion for improving engineering productivity and efficiency through a data-driven approach.