Sr. Applied Scientist, SSG Science

Amazon Inc.

Bengaluru

On-site

INR 3,000,000 - 5,500,000

Full time

14 days+

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Job summary

Amazon Devices is an inventive R&D team building edge Gen AI for Kindle, Fire and Echo devices. You will quantify and optimize models for edge platforms, work with HW/Compiler teams, and publish open source. Excellent candidates have deep learning expertise and production experience.

Join a collaborative environment to push the state of the art, present at conferences, and contribute to industry-leading edge ML architectures and tools.

Qualifications

  • 3+ years building ML models for business applications.
  • PhD or Master's with 4+ years of applied research.
  • Experience programming in Java, C++, Python.
  • Productionizing ML models through full lifecycle.

Responsibilities

  • Quantize, prune, distill Gen AI models for edge platforms.
  • Understand Neural Edge Engine to invent optimizations.
  • Analyze deep learning workloads and map to edge engine.
  • Collaborate with engineers and product teams to build ML-centric solutions.
  • Publish in open source and present at key ML conferences (NeurIPS/ICLR/MLSys).

Skills

ML model development
Deep learning
Edge optimization
Production ML

Education

PhD
Master's + 4+ years

Tools

Java
C++
Python

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
  • 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.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Veterans, military spouses, and people with disabilities are encouraged to apply.

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