

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• Study and apply first principles of Information Theory, Scientific Computing, and Non-Equilibrium Thermodynamics to model optimization problems• 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
- Bachelor's degree or above in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- PhD, or a Master's degree and experience with popular deep learning frameworks such as MxNet and Tensor Flow
- Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
- Experience in Java, C++, Python, or a related language
- 1+ years of industry or academic research experience
- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience in software development
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
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