Nvidia Senior Software R&D Engineer VLSI Physical Design 

United States, Texas 

580514262

What you’ll be doing:

• Invent new optimization engines that fuse traditionally independent engines (e.g., co-optimization of legalization and sizing) with the objective of increasing chip frequency while minimizing power consumption across a suite of internal optimization tools. These tools already outperform the industry's alternatives in high capacity timing closure and will advance even further with your contributions.

• Improve algorithms (in C++) for gate-level sizing, buffering, useful clock skew, cell legalization, power minimization, ECO routing, and incremental parasitic extraction.

• As with any software engineering team, we do write a lot of code, but this is broader than a typical CAD or EDA role. Instead, we as a team own the whole process from discovery and invention of new optimization opportunities, to developing solutions and working directly inside design teams to facilitate deployment.

What we need to see:

• BS, MS, PhD or equivalent experience in Electrical Engineering or Computer Science

• 10+ years in VLSI algorithms development using C++

• Strong understanding of VLSI timing optimization and related concepts, including cell libraries, interconnect models, crosstalk, glitches, IR drop, timing constraints, corners, congestion, etc.

• Familiarity with design implementation tools such as ICC2, Innovus, PrimeTime, Tempus, and StarRC and typical design flows written in Perl, Tcl, and Python

• Strong communication and interpersonal skills

Ways to stand out from the crowd:

• C++14 or newer experience, such as lambdas and concurrency

• Detailed understanding of how multiple Physical Design steps interact and how they can potentially be fused together to form hybrid engines that result in better PPA

• Experience in high performance software design including multithreading, distributed computing, efficient memory and I/O use, etc.

• Highly driven to craft outstanding software towards improving PPA with a dedication to continuous improvement

• Experience with reinforcement learning, GNNs (Graph Neural Networks), and other relevant machine learning frameworks, especially as applied to physical design