Senior Research Scientist - On-Device Machine Learning

Key Skills

Deep Learning, Machine learning techniques, PyTorch, Signal processing, TensorFlow

Job Description

Title: Senior Research Scientist - On-Device Machine Learning

Company: Samsung Research America (SRA)

Lab: Artificial Intelligence Center

Location: Mountain View, CA

Lab Summary:

The AI center at Samsung Research America (SRA), located in Mountain View, CA, is looking for an outstanding and highly self-motivated researcher/engineer. We are looking for candidates with machine learning and deep learning background. You will work with a team of research scientists and engineers tackling real-world problems involving Samsung’s Artificial Intelligence initiatives. You will be involved in very promising team projects with talented people at Samsung. You will benefit a lot by working in a fun and creative environment. The AI research center is a key part of Samsung’s global R&D effort and aims to have influence on future Samsung products reaching hundreds of millions of users worldwide.

Common Essential Duties & Responsibilities:

• Design, develop and implement novel efficient deep learning/machine learning algorithms at extremely low power for various applications including vision, audio, sensor data, etc.

• Develop and implement efficient deep learning/machine learning models on small, low-powered devices like microcontrollers, IOT devices, etc.

• Generate creative solutions (patents) and publish research results in top conferences (papers).

Background / Experience:

• Experience in efficient deep/machine learning model/architecture design for edge devices (e.g., MobileNet, ShuffleNet, etc.)

• Experience in digital signal processing and leveraging DSP to design lightweight deep/machine learning models

• Experience in model compression techniques (e.g., pruning, quantization, knowledge distillation, SVD, etc.)

• Experience in software-hardware codesign for efficient machine/deep learning

• Experience in developing and implementing deep/machine learning models on microcontrollers, IOT devices, etc.

• Experience in privacy-preserving learning (e.g., federated learning)

• Experience in developing and implementing efficient on-device ML/DL and TinyML model training pipeline

• Experience in platform-aware model optimization (e.g., low-precision training/inference)

• Experience in efficient hardware accelerator design for neural computing on mobile devices

• Proficiency in on-device ML and TinyML libraries (e.g., TensorFlow Lite, TensorFlow Lite for Microcontrollers, PyTorch Mobile, etc.)
• Proven track record of research/publications on machine learning and artificial intelligence field (NeurIPS, ICLR, ICML, AAAI, IJCAI, CVPR, ACL, etc.)