Senior GPU Memory Architect

NVIDIA•Durham, NC

10d•$184,000 - $356,500

About The Position

NVIDIA is seeking a motivated architect to work with a team in solving complex problems while optimizing performance, area, complexity, and power on leading-edge silicon processes. This GPU memory architecture team creates new, innovative products tailored to NVIDIA’s world-changing solutions for autonomous vehicles, AI, gaming, mobile systems.

Requirements

Master degree or equivalent experience in Electrical Engineering, Computer Science, Computer Engineering or related field.
6+ years of meaningful work experience in GPU or CPU Architecture and development specifically in the area of memory caching and interconnects.
Strong communication and interpersonal skills are required along with the ability to work in a dynamic, product oriented, distributed team.

Nice To Haves

A PhD with a focus in computer architecture is a plus.
Your history of successfully mentoring junior engineers and interns is a huge plus.
Experience with hardware memory management unit, prefetching, or memory subsystems.
Practical experience with multi-core systems and memory coherency.

Responsibilities

Developing architecture and micro-architecture to improve the state-of-the-art in GPU memory system and memory management optimizing along the axes of performance, power efficiency, complexity, area, effort, and schedule.
Participating in performance simulation of features to improve memory access efficiency.
Implementing and maintaining high-level functional and performance models.
Analyzing benchmarks, application workloads and performance simulation results to identify areas for microarchitecture optimizations.
Defining and performing experiments to study the machine in action, presenting experiment results to the larger group and proposing mechanisms for improvement.
Creating architectural specifications and customer-facing documents.
Working with partners in the industry to generate specifications which take into account software interfaces to the GPU.
Debugging performance and functional issues with high-level models, RTL simulation, and silicon.