Applied Scientist, Ssg Science

Amazon Amazon · Big Tech · Sunnyvale, CA · Applied Science

Applied Scientist role focused on optimizing Generative AI models for edge devices, involving quantization, pruning, distillation, and fine-tuning. The role also requires understanding and inventing optimization techniques for custom ML hardware and collaborating with hardware architects and compiler engineers. The goal is to develop production-ready edge models and publish research findings.

What you'd actually do

  1. Quantize, prune, distill, finetune Gen AI models to optimize for edge platforms
  2. Fundamentally understand Amazon’s underlying Neural Edge Engine to invent optimization techniques
  3. Analyze deep learning workloads and provide guidance to map them to Amazon’s Neural Edge Engine
  4. Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, Non Equilibrium Thermodynamics
  5. Train custom Gen AI models that beat SOTA and paves path for developing production models

Skills

Required

  • Designing experiments and statistical analysis of results
  • CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Programming in Java, C++, Python or related language
  • Algorithms and data structures
  • Parsing
  • Numerical optimization
  • Data mining
  • Parallel and distributed computing
  • High-performance computing

Nice to have

  • Unix/Linux
  • Professional software development

What the JD emphasized

  • publish in open source and present on Amazon's behalf at key ML conferences - NeurIPS, ICLR, MLSys
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • Gen AI on edge
  • optimize them while doing co-designed with custom ML HW
  • develop the next generation of edge models