Sr. Applied Scientist, SSG Science

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 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
• What will you do?
• - Quantize, prune, distill, finetune Gen AI models to optimize for edge platforms
• - Fundamentally understand Amazon’s underlying Neural Edge Engine to invent optimization techniques
• - Analyze deep learning workloads and provide guidance to map them to Amazon’s Neural Edge Engine
• - Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, Non Equilibrium Thermodynamics
• - Train custom Gen AI models that beat SOTA and paves path for developing production models
• - Collaborate closely with compiler engineers, fellow Applied Scientists, Hardware Architects and product teams to build the best ML-centric solutions for our devices
• - Publish in open source and present on Amazon's behalf at key ML conferences - NeurIPS, ICLR, MLSys.

Basic qualifications

• - 3+ years of building machine learning models for business application experience
• - PhD, or Master's degree and 4+ years of applied research experience
• - Experience programming in Java, C++, Python or related language
• - Experience with neural deep learning methods and machine learning
• - Prior experience in productionizing ML models and managing their full lifecycle from development to deployment

Preferred qualifications

- Understanding and preferably hands-on experience with recent methods for inference optimization, including Mixture-of-Experts (MoE), Diffusion Models for Language Generation, etc.

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