Research Scientist, Ssg Science
21 hours ago
**DESCRIPTION**:
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for exceptional early career research scientists to join our Applied Science team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture. Work hard. Have Fun. Make History.
Key job responsibilities
Key Job Responsibilities:
- Understand and contribute to model compression techniques (quantization, pruning, distillation, etc.) while developing theoretical understanding of Information Theory and Deep Learning fundamentals
- Work with senior researchers to optimize Gen AI models for edge platforms using Amazon's Neural Edge Engine
- Assist in research projects involving custom Gen AI model development, aiming to improve SOTA under mentorship
- Co-author research papers for top-tier conferences (NeurIPS, ICLR, MLSys) and present at internal research meetings
- Collaborate with compiler engineers, Applied Scientists, and Hardware Architects while learning about production ML systems
- Participate in reading groups and research discussions to build expertise in efficient AI and edge computing
**BASIC QUALIFICATIONS**:
- Bachelor's degree or above in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Strong understanding of deep learning fundamentals, linear algebra and statistics.
- Experience working with deep learning frameworks such as Pytorch or Tensorflow.
- Proven experience in writing high-quality code in C/C++/Python.
- Prior academic or industrial research experience is required.
**PREFERRED QUALIFICATIONS**:
- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Hands-on experience with model compression techniques (quantization/pruning/distillation).
- Experience training or fine-tuning large language models.
- Publications at top-tier conferences (NeurIPS, ICLR, ICML).
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