Applied Scientist II - Amz9674020

Amazon Amazon · Big Tech · Mountain View, CA · Corporate Operations

Applied Scientist II role focused on designing, developing, and deploying data-driven models for ML and NL applications, with a strong emphasis on generative AI, NLP, and large-scale model training and deployment. The role involves researching and implementing novel ML approaches, fine-tuning foundation models, developing custom algorithms for model optimization, and conducting applied research on generative AI architectures and training strategies. Mentoring junior scientists is also a key responsibility.

What you'd actually do

  1. Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications.
  2. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering.
  3. Routinely build and deploy ML models on available data.
  4. Research and implement novel ML and statistical approaches to add value to the business.
  5. Mentor junior engineers and scientists.

Skills

Required

  • programming in Java, C++, Python, or equivalent programming language
  • conducting the analysis and development of various supervised and unsupervised machine learning models for moderately complex projects in business, science, or engineering

Nice to have

  • Bayesian models
  • deep neural networks
  • optimization methods
  • generative AI
  • natural language processing
  • large-scale model training
  • fine-tune foundation models
  • LoRA
  • parameter-efficient methods
  • custom machine learning algorithms
  • model optimization
  • distillation
  • hardware-specific optimizations
  • retrieval-augmented generation
  • reinforcement learning from human feedback
  • novel training methodologies
  • reinforcement learning techniques

What the JD emphasized

  • design, development, evaluation, deployment and updating
  • statistical modeling techniques
  • ML techniques
  • ML models
  • ML and statistical approaches
  • ML algorithms
  • ML principles
  • model optimization
  • generative AI architectures
  • training strategies
  • optimization techniques
  • retrieval-augmented generation
  • fine-tuning methodologies
  • reinforcement learning from human feedback
  • Mentor junior engineers and scientists

Other signals

  • Generative AI
  • Large-scale model training
  • Fine-tuning foundation models
  • Applied research on generative AI architectures