Deep Learning Systems Architect

(US) Santa Clara CA , Austin TX, Portland OR, Fort Collins CO
Engineering – Silicon Engineering /
Full-time /
Hybrid
In this role, the candidate will be required to understand Deep learning workload characteristics and have the hands-on ability to measure, analyze and use the data to project and estimate the power and performance of the latest DL workloads. 

Responsibilities

    • The ideal candidate will have both software and hardware background to do sensitivity analysis for both hardware knobs and understand how to measure and improve the performance of DL workloads.
    • The candidate should have worked on simulators and have experience with benchmarking DL models.
    • The ideal candidate should have at least 5+ years of experience working on performance analysis of DL workloads running workloads on accelerators and improving them.
    • Programming and debugging code written in python/C++/CUDA/HIP/OpenCL will be required as well as ability to model and work with the hardware teams to measure power and performance of key kernels running on RTL and performance simulators
    • Knowledge of performance and power modeling is a plus.
    • Solid understanding of the fundamentals of computer architecture, memory hierarchy, caches and fabrics is a prerequisite for the role. 

Requirements

    • Excellent skills in problem solving, written and verbal communication, excellent organization skills, and highly self-motivated.
    • Ability to work well in a team and be productive under aggressive schedules

Education and Experience

    • PhD, Master’s Degree in Computer Engineering / Computer science with 5+ years of experience working on DL models.
    • Coursework on computer architecture, parallel computing , compilers and digital design is required.