On-Device AI Engineer

This job posting expired and applications are no longer accepted.
December 20, 2019
Staines-Upon-Thames, United Kingdom
Job Type


Purpose of the Role:

We are currently looking for exceptional AI and ML experts to join our growing team in Staines-upon-Thames. This is a good opportunity to work on next generation mobile devices from Samsung.

We need enthusiastic engineers with a strong academic and experience in Machine Learning frameworks and tools. The candidate will contribute to the development and optimisation of models and ML frameworks for Samsung’s new mobile devices.

As and On-Device AI Engineer you will:

  • Develop innovations to optimize ML frameworks and libraries to enable inference on device and deliver class-leading performance
  • Analyse models and identify optimisations
  • Have technical responsibility for one or more significant sections of the assigned project
  • Translate complex functional and technical requirements into detailed design

Skills and Qualifications

Skills and attributes:                    

Required Skills

  • A degree in Computer Science, Electronics, Natural Science, Mathematics, Engineering or any related discipline (an equivalent period of industrial experience may be substituted).
  • A proven track record in real time embedded software development preferably for mobile devices with an understanding of embedded development environments.
  • Experience developing Machine Learning frameworks (contribution to – Caffe / Caffe2, TensorFlow / TensorFlow Lite etc.)
  • Experience with heterogeneous programming (NEON/SIMD, OpenCL/CUDA, compute shaders)
  • Excellent C, C++ programming experience
  • Excellent communication, team work and a results-oriented attitude
  • Proficiency in problem solving and debugging

Desirable Skills

  • Experience with CNN / RNN / Auto Encoders / LTSM
  • Experience with computer vision algorithms
  • Experience working with ARM 64-bit architecture
  • Experience developing system software for Android OS
  • Familiarity with CPU and GPU hardware architectures
  • Exposure to Machine Learning & Neural Networks

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