Applied Scientist II, SSG Science

Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning · Software Engineering

In short

Amazon is seeking an Applied Scientist II to develop state-of-the-art Gen AI models for edge devices. The role involves optimizing models for edge platforms, understanding custom ML hardware, and collaborating with cross-functional teams. A PhD or Master's degree with significant experience in ML and related fields is required.

Responsibilities

Quantize, prune, distill, and finetune Gen AI models for edge platforms.
Understand Amazon's Neural Edge Engine to invent optimization techniques.
Analyze deep learning workloads and map them to Amazon's Neural Edge Engine.
Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, and Non Equilibrium Thermodynamics.
Train custom Gen AI models that achieve state-of-the-art performance.
Collaborate with compiler engineers, Applied Scientists, Hardware Architects, and product teams.
Publish in open source and present at ML conferences (NeurIPS, ICLR, MLSys).

Requirements

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

Desired Qualifications

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

Skills

JavaC++PythonMachine LearningDeep LearningAlgorithmsData StructuresData MiningParallel ComputingDistributed ComputingHigh-Performance ComputingLinear AlgebraCalculus

#applied scientist#machine learning#deep learning#gen ai#edge devices#hardware#science

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