Senior Applied Scientist - Machine Learning and AI Optimization

Amazon India Limited; Bengaluru; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

Amazon India Limited in Bengaluru seeks a Sr. Applied Scientist to advance Gen AI on edge devices across Kindle family, Fire tablets, Echo, and Astro. You will design and optimize state-of-the-art edge ML models, co-design with custom ML hardware, and drive end-to-end experiments.

Collaborate with hardware, compiler engineers, and product teams; publish in open source and present at NeurIPS/ICLR/MLSys. The role requires PhD or equivalent research, and strong software skills in Python, Java, and

Qualifications

• 3+ years of building machine learning models for business applications.
• PhD or Master's degree with 4+ years of applied research experience.
• Proficiency in Java, C++, and Python.
• Experience with neural deep learning methods and ML.
• Experience deploying ML models end-to-end, from development to production.

Responsibilities

• Quantize, prune, and finetune Gen AI models for edge platforms.
• Understand and optimize the Neural Edge Engine.
• Analyze deep learning workloads and map them to edge hardware.
• Apply information theory and scientific computing concepts to ML tasks.
• Train custom Gen AI models that beat state-of-the-art baselines.
• Collaborate with compiler engineers, hardware architects, and product teams.
• Publish open-source work and present at major ML conferences.

Skills

Java

C++

Python

Machine Learning

Deep Learning

Productionizing ML

Education

PhD

Master's degree

Job description

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

• 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. Experience Level Senior Level