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

Basic Qualifications

- 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

Preferred Qualifications

- 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