Applied Scientist, SSG Science

Amazon; Bengaluru; On-site; INR 350,000 - 700,000; Full time

Job summary

Amazon Devices in Bengaluru is seeking an Applied Scientist to advance Gen AI on edge devices. You will work on quantization, pruning, distillation and finetuning models for edge platforms, and collaborate with hardware and compiler teams.

Ideal candidates have a PhD or MS with postdoc or industry research, strong ML theory, and publications; you will contribute to productionizing models and presenting at top conferences.

Qualifications

• 2+ years building ML models for business applications.
• PhD or Master's with 2+ years of applied research.
• Publications or patents at top-tier conferences or journals.
• Proficiency in Java, C++, Python.

Responsibilities

• 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.

Skills

Machine Learning

Python

Java/C++

Numerical optimization

Distributed computing

Education

PhD or MS + 2+ years research

Tools

Neural Edge Engine

Git

Job description

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

• 2+ years of building machine learning models for business application experience
• PhD, or Master's degree and 2+ years of applied research 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.)

Preferred Qualifications

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

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Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.