Senior Principal Data Scientist, Machine Learning – A001829
Location: Campbell, CA, USA
Job Type: Full Time Employee
Career Level: Experienced
Development of Fixed Point Models of Deep Neural Networks (DNN) using C/C++ and MATLAB. Perform fixed point training and inferencing; compare accuracy of fixed point training and inferencing against their floating-point counterparts. Explore and develop techniques for accelerating training for a variety of fixed point DNN models, while maintaining numerical stability that lead to convergence. Benchmark performance and compare against other platforms from the competitors. Understand and master the tradeoffs between computation and storage requirements as it relates to the requirements for shrinking model sizes for Inference type applications. Explore and implement methods for efficient Transfer Learning.
Required Skills and Experiences
- Develop and test C/C++ and MATLAB programs as a member of the development team working on the fixed point models of DNNs. Compare/benchmark the results against their floating-point counterparts. Participate in the applied research and/or advanced development activities in Machine Learning Algorithms. Must be a team player.
- Knowledge of Machine Learning, especially Training and Inference processing for Convolutional Neural Networks (CNN); Proficiency with C/C++ is a must. Must be familiar with with different DNNs, e.g. AlexNet, VGG, Inception, ResNet, SqueezeNet etc. Experience with Keras, Tensor flow, Caffe etc. is required. Proficiency with Python is desired. Knowledge of Linear Algebra and Gradient Descent Methods is required. Conference and/or Journal Publication(s) in Machine Learning is required.
- Minimum education level required: Ph.D or MS in Computer Science or Electrical Engineering.
- Minimum years of experience required: Ph.D. plus 5 years or MS plus 10 years.
- Exposure to fixed point implementations is preferred
- Exposure to mapping of Algorithms on a GPU is a plus