We are looking for a first-class DL Performance architect to drive the performance analysis and optimization of the state of art inference network on our GP: identify HW, SW performance limiters of DL networks, prototype the key primitives and guide the design of next generation architecture and DL software optimization.

　　What you’ll be doing:

　　Establish deep learning applications and use-cases for performance analysis, modelling, and projections

　　Analyzing and proposing both SW and HW optimizations for deep learning applications

　　Specify hardware/software configurations and metrics to analyze performance, power, accuracy and resiliency in existing and future uni-processor and multiprocessor configurations

　　Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, library, and compiler teams

　　What we need to see:

　　MS or PhD in relevant discipline (CS, EE, Math) or equivalent experience with 2+ years of experience

　　Track record of designing architectures to accelerate computational demanding algorithms and applications

　　Strong background in computer architecture

　　Expert mathematical foundation in machine learning and deep learning

　　Strong programming skills in C, C++, Perl, or Python

　　Ways to stand out from the crowd:

　　Prior experience working on assembly level performance optimization

　　Experience working with deep learning frameworks like Caffe, TensorFlow and Torch

　　Familiarity with GPU computing (CUDA, OpenCL) and HPC (MPI, OpenMP)

　　Background with systems-level performance modeling, profiling, and analysis

　　Experience in characterizing and modeling system-level performance, executing comparison studies, and documenting and publishing results

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