Bachelor’s degree or foreign equivalent in Electrical engineering, Electronics engineering or related field and 1 year of experience in the job offered or related occupation. Education and/or experience must include: Utilizing C and Python to develop performance analysis tools and automation scripts for GPU microarchitecture evaluation. Compiler toolchains, including experience analyzing instruction scheduling, register allocation, and code generation for GPU compute workloads.
Utilizing SystemVerilog or Verilog, including experience reading and analyzing RTL to identify and debug hardware-level performance bottlenecks. Experience in GPU microarchitecture analysis, including utilizing performance counters to evaluate throughput, latency, and execution unit utilization. Experience in memory hierarchy and NUMA architecture analysis, including cache behavior, memory bandwidth characterization, and error correction mechanisms.
Utilizing Python (Pandas, Matplotlib) and data visualization tools to build performance dashboards and report regression trends to engineering teams. Utilizing scripting languages including Python and Shell to automate performance regression testing, benchmark execution, and results collection. Experience in full-stack hardware/software performance analysis, including correlating compiler output to microarchitecture execution behavior across silicon and pre-silicon environments.
Utilizing Git and code review processes, including experience collaborating on multi-engineer codebases to implement and validate performance improvements.