Performance and Modeling Engineer
Confirmed on the employer's own hiring board on Nov 7, 2026. First seen by Alion on Nov 5, 2026. Apple scores A on the Alion truth index.
Imagine what you can do here. Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn't have imagined, and now, can't imagine living without. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do.
Description
APPLE INC has the following available in Austin, Texas. Working with driver, compiler and deigns engineers to improve GPU performance. Validating the functionality and quantify the performance benefit of the features and algorithms. Implementing in hardware and software; and conducting directed studies and help provide solutions to performance challenges unique to mobile GPUs that are aimed at driving the architecture of next generation mobile products via measurements, simulations, benchmarking, or competitive analysis. Debugging potential GPU performance issues and fix them with design engineers. Conducting GPU performance contents to measure GPU performance. Working with software teams to develop GPU model in different sections. Developing tools to aid in efficient measurement, communication and improvement of GPU performance. 40 hours/week.
Minimum Qualifications
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.
Preferred Qualifications
N/A