Intel Deep Learning Computational Graph Engineer in Santa Clara, California
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
Who we are looking for:
• Bachelor’s, Master’s, or Ph.D. in Computer Science or Computer Engineering
• Comfortable manipulating representations of programs
• 5+ years developing commercial quality system software, e.g. compilers, debuggers, profilers
• Comfortable with one or more deep learning frameworks such as neon, TensorFlow, Caffe2, CNTK, or Torch
• Strong programming skills in C++, familiarity with Python preferred • Experience with LLVM, HPC, MPI, distributed systems, MKL, MKL-DNN, CUDA, cuDNN, nervanaGPU is preferred
Inside this Business Group
Intel AI, 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) with the Intel AI products portfolio. 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 AI is inside AI and leading the next evolution of compute.
US, California, San Diego; US, Oregon, Hillsboro;
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance..