Applied Scientist II, 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 Amazons underlying Neural Edge Engine to invent optimization techniques
- Analyze deep learning workloads and provide guidance to map them to Amazons 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 Amazons behalf at key ML conferences - NeurIPS ICLR MLSys.

- 2 years of building models for business application experience
- PhD or Masters degree and 4 years of CS CE ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java C Python or related language
- Experience in any of the following areas: algorithms and data structures parsing numerical optimization data mining parallel and distributed computing high-performance computing
- Strong foundation in relevant mathematical concepts (linear algebra calculus etc.)

- Experience in professional software development
- Prior experience in productionizing ML models and managing their full lifecycle from development to deployment

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