This position is for a Deep Learning Software Engineer in Intel Movidius NN software engineering team creating software for Intel's edge inference VPU products.
Your responsibilities will include :
Implementation of embedded run time scheduling for hardware.
Design and code of distributed real-time embedded inference kernels, designed and optimized for a specific hardware architecture, implementing both basic and cutting edge network features for multiple types of numerical precision.
Profiling and optimization of layer and full network operation.
Work in both pre and post silicon environments.
Unit test, numerical accuracy and performance verification of work.
Must have either a BS or MS in Computer Science, Computer Engineering or similar field.
3-5 years hands-on coding experience in C++ programming language.
Significant DSP, embedded RTOS or bare metal coding and debugging experience.
Numerical coding experience in mixed precision such as float16, bfloat16, int8 and other models.
Familiarity with Deep Learning frameworks (TF, Caffe, PyTorch, OpenCV, etc.).
Development experience in a Linux environment.
The following is highly desired :
Previous Deep learning layer implementation experience.
Modern compiler architecture and back-end coding experience.
Spoken and written English : upper-intermediate level or advanced.
Inside this Business Group
Employees of the Internet of Things Solutions Group (IOTG) have an exciting opportunity before them : To grow Intel's leadership position in the rapidly evolving IoT market by delivering the best silicon, software and services that meet a wide range of customer requirements - from Intel® Xeon® to Intel® Quark®.
The group, a fresh, dynamic collaboration between Intel's Intelligent Solutions Group and Wind River Systems, utilizes assets from across all of Intel in such areas as industrial automation, retail, automobiles and aerospace.
The IOTG team is dedicated to helping Intel drive the next major growth inflection through productivity and new business models that are emerging as a result of IoT.