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Machine Learning and AI Compiler and Frameworks Engineer

Machine Learning and AI Compiler and Frameworks Engineer
by Daniel Nenni on 08-10-2020 at 9:13 pm

Website Cadence

Your responsibilities will include:

Developing a deep learning compiler stack that takes neural network descriptions (CNNs/RNNs) created in frameworks such as Caffe, PyTorch, TensorFlow, etc. and converts them into code suitable for execution on special-purpose and embedded platforms
Developing optimized implementations of a variety of neural-network operations and integrating them into a runtime framework
Developing new optimization techniques and algorithms to efficiently map CNNs onto a wide range of Tensilica Xtensa processors and specialized HW
Benchmarking end-to-end network performance on a variety of DSP and special-purpose accelerator platforms
Enhancing the framework to improve overall functionality and performance on the various hardware platforms
Devising multiprocessor/multicore partitioning and scheduling strategies
Developing complex programs to validate the functionality and performance of the CNN application programming kit
Working with hardware designers to identify opportunities for additional hardware acceleration of neural network functions
Working with industry-leading partners and customers to design and standardize neural network APIs

Required and desired qualifications:

A high level of C and C++ programming expertise with 3-5+ years of experience is required; Python experience highly desired
Expertise in software development on Linux and Windows systems including, test, debug and release is required
3-5+ years of experience working on a production compiler is highly desired
Prior work with CNNs and familiarity with deep learning frameworks (TensorFlow, Caffe/2, etc.) is a strong plus
Knowledge of and experience with a state-of-the-art compiler stack such as LLVM is highly desired
Experience programming and optimizing for embedded platforms such as DSPs with DMA engines highly desired
Experience implementing compilation techniques such loop optimization, polyhedral models, and IR construction/transition/lowering techniques is desired
Familiarity with the state-of-the-art deep learning compilation approaches (XLA, Glow, etc.) is a plus
Familiarity with various deep learning networks and their applications (Classification/Segmentation/Object Detection/RNNs) is a plus
Knowledge of neural net exchange formats (ONNX, NNEF) is a plus
Familiarity with Android system programming and the Android Neural Network API (NNAPI) is a plus

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