Applied Scientist, SSG Science

Optimize and fine-tune Generative AI models for deployment on edge platforms, focusing on quantization, pruning, distillation, and custom training.

Develop and invent optimization techniques closely integrated with Amazon's Neural Edge Engine based on a deep understanding of deep learning workloads and scientific principles.

Collaborate with cross-functional teams including compiler engineers, hardware architects, and product teams, and contribute to scientific publications and conferences.

Bachelor's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field.

Experience programming in Java, C++, Python, or related language.

Work Experience Required: Not explicitly mentioned in the JD.

No explicit notice period or location requirements mentioned.

Strong ability to implement and optimize machine learning algorithms, preferably demonstrated through self-developed code and experience with ML toolkits.

Researchers or applied scientists with a track record or aspiration to publish in top-tier ML venues (e.g., NeurIPS, ICLR, MLSys).

Experience working in a hardware-software co-design environment involving deep learning model optimization for edge devices.