Senior Software R&D Engineer, VLSI Physical Design
$168,000–$264,500 year
HybridAustin, Texas, United States or Santa Clara, California, United States
Job Summary
Invent new optimization engines that fuse traditionally independent VLSI physical design algorithms, such as co-optimization of legalization and sizing, to increase chip frequency while minimizing power consumption. Improve C++ algorithms for gate-level sizing, buffering, cell legalization, power minimization, ECO routing, and incremental parasitic extraction. Own the whole process from discovery and invention of new optimization opportunities to developing solutions and working directly inside design teams to facilitate deployment. Focus on high-performance software design including multithreading, distributed computing, and efficient memory and I/O use to advance internal tools that outperform industry alternatives in high capacity timing closure.
Required Qualifications
- BS, MS, PhD or equivalent experience in Electrical Engineering or Computer Science
- 6+ 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
Desired Qualifications
- 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
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