This is an entry level position and will be compensated accordingly.
What you will be working on:
• Compilation of deep learning model descriptions in multiple frameworks into efficient code for execution in a variety of environments
• Intermediate representations and APIs to allow new frameworks and environments to be defined
• Collaborating with or working directly on data science, compilers, cloud software, distributed systems, system software, QA, technical writing
• Making a variety of machine learning hardware platforms more efficient and easier to use
You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Experience listed below would be obtained through a combination of your school work/classes/research and/or relevant previous job and/or internship experiences.
Inside this Business Group
• Master’s, or Ph.D. in Computer Science or Computer Engineering or related field
• 1-2 years of experience in the following areas:
. Developing commercial quality system software, e.g. compilers, debuggers, profilers
• Experience with one or more deep learning frameworks such as neon, TensorFlow, Caffe2, CNTK, or Torch
• Programming skills in C++, familiarity with Python preferred
• Experience with LLVM, HPC, MPI, distributed systems, MKL, MKL-DNN, CUDA, cuDNN, nervanaGPU is preferred
• Comfortable manipulating representations of programs
Intel Nervana, leveraging Intel’s world leading position in silicon innovation and proven history in creating the compute standards that power our world, is transforming Artificial Intelligence (AI). Harnessing silicon designed specifically for AI, end-to-end solutions that broadly span from the data center to the edge, and tools that enable customers to quickly deploy and scale up, Intel Nervana is inside AI and leading the next evolution of compute.
US, Arizona, Phoenix; US, Oregon, Hillsboro; US, California, Santa Clara;