Architect - System Performance Verification and Analysis
NVIDIA CorporationMatch Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessStrong Tier-1 brand, mid-level experience requirement, and Bengaluru metro location increase candidate competition.
Requires specialized SoC, RTL, and GPU performance expertise, limiting transferability across industries.
Mandatory 3+ years plus specific SoC, RTL, and programming skills create strict technical filters.
Job Description
Structured overview of role & requirementsAbout This Role
Own full-chip SoC performance verification from pre-silicon models through silicon bringup, including driving performance test plans reflecting real customer workloads.
Identify and debug performance bottlenecks and regressions across CPU, GPU, memory subsystems, fabric, using waveform analysis, signal queries, and profiling.
Develop and maintain performance workloads, test suites, automation infrastructure and improve methodologies leveraging AI-assisted tools to accelerate analysis and reduce verification turnaround time.
Minimum Requirements
Bachelor's or Master's in Electrical Engineering, Computer Science, or related field.
3+ years relevant experience in SoC or system-level architecture, performance verification, or hardware validation.
Strong understanding of SoC architecture including GPU/CPU pipelines, memory subsystems (caches, DRAM controllers, coherency), Network-on-Chip/fabric, and high-speed IO interfaces.
Hands-on RTL simulation and debug experience, programming skills in Python, C/C++, scripting in Bash or Python.
Ideal Candidate Profile
Proven experience in RTL-level performance debug with precise signal queries and waveform interpretation for complex system interactions.
Background in system-level performance analysis for GPUs, AI accelerators, or high-bandwidth memory with bottleneck identification across concurrent engines.
Demonstrated use of AI/LLM-assisted engineering tools to improve productivity and engineering efficiency in performance verification contexts.
