Amazon

Applied Scientist, SSG Science

Posted On

Sept. 12, 2025

Location

Bengaluru, Karnataka

Experience Level

Freshers/Experienced

Job Type

Machine Learning Science

Apply By

Sept. 30, 2025

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
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.- 3+ years of building models for business application experience
- 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
- Bachelor's degree in computer science, electrical engineering, or related field
- Strong foundation in related mathematical concepts (e.g. linear algebra, calculus, etc.)

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