Requirements

Currently pursuing a BS, MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, Electrical and Computer Engineering, or a related field
At the end of the internship, you must return to school to continue your education or the internship must be the last requirement for you to graduate
Strong coding skills with C or C++
Strong understanding of common data structures, algorithms, and design patterns
A curiosity about GPU / CPU / SOC architecture and micro-architecture
Knowledge of scripting languages such as Perl, Python or Ruby

Nice to Haves

Strong interpersonal and analytical skills
Ability to work well within a team and be productive under tight schedules
Familiarity with SystemVerilog / Verilog / VHDL
Experience optimizing rendering/parallel compute algorithms, drivers and/or compilers for one or more GPU architectures
Prior experience on hardware architectural modeling and hardware description languages
Experience with one or more GPU APIs (Metal, DX12, Vulcan, CUDA, OpenGL, OpenCL)
Knowledge of performance simulation environments
Knowledge of software design patterns

Develop GPU workloads, automated flows, and tools to support the verification of GPU designs
Analyze performance of existing and future workloads and propose new features to boost GPU performance
Collaborate with architects, micro-architects, and designers to improve GPU designs
Work with verification and software teams to ensure functionality, performance, and power efficiency of GPU designs
Use performance and energy models to evaluate and tune new ideas for GPU performance enhancements