# Staff R&D Engineer, Accelerated Verification Tools

**Company**: Synopsys
**Location**: Sunnyvale
**Work arrangement**: onsite
**Experience**: staff
**Job type**: employee
**Salary**: $138,000-$207,000
**Category**: Engineering
**Industry**: Technology
**Ticker**: SNPS
**Wikidata**: https://www.wikidata.org/wiki/Q2303478

**Apply**: https://careers.synopsys.com/job/sunnyvale/staff-r-and-d-engineer-accelerated-verification-tools-16955/44408/94200451968
**Canonical**: https://yubhub.co/jobs/job_0ea79b8f-92e

## Description

As a Staff R&D Engineer, Accelerated Verification Tools, you will develop high-performance simulation solutions utilizing GPU acceleration and parallel programming to tackle complex EDA challenges.

You will be responsible for designing and optimizing accelerated verification tools using massive parallelism and GPU technologies, implementing and troubleshooting advanced graph algorithms to manage complex hardware designs, developing GPU kernels (CUDA/HIP/DPC++) to reduce simulation runtimes, leveraging multi-threaded programming (OpenMP/TBB) to maximize throughput, and debugging functionality and performance of compiler enhancements and optimizations.

Your impact will be accelerating customer chip development cycles and enabling next-generation silicon design, driving innovation in GPU-powered algorithms, and empowering engineering teams to achieve faster RTL sign-off.

To succeed in this role, you will need proficiency in C/C++ and performance profiling, knowledge of GPU computing (CUDA/HIP/DPC++) and parallel memory models, experience with multi-threaded programming (OpenMP, TBB), knowledge in HDL (SystemVerilog/VHDL) and compiler design, an AI-First Mindset: proficiency in using modern AI-assisted coding tools like Cursor or Claude Code to accelerate development cycles and maintain high code quality, and a Growth Mindset: we welcome candidates with HPC and algorithm expertise who have an interest in mastering the hardware verification domain.

Desirable skills include experience implementing high-performance graph algorithms on GPU and multicore architectures, familiarity with distributed computing frameworks and memory-efficient data structures.

Education and experience requirements include a BS in CS/EE or related field with 5+ years of experience, a MS in CS/EE or related field with 3+ years of experience, or a PhD in CS/EE with 0-1 years of experience.

## Skills

### Required
- C/C++
- GPU computing
- multi-threaded programming
- HDL
- compiler design
- AI-assisted coding tools

### Nice to have
- high-performance graph algorithms
- distributed computing frameworks
- memory-efficient data structures