Applied Scientist, Agi Customization Services

Amazon Amazon · Big Tech · Cambridge, MA · Applied Science

Applied Scientist role focused on developing and customizing large language models for enterprise use cases, involving techniques like supervised fine-tuning, reinforcement learning, and knowledge distillation. The role requires building enterprise-ready tooling, optimizing models, and contributing to responsible AI toolkits.

What you'd actually do

  1. Contribute to the development of novel customization techniques including extended post-training, continued pre-training, and advanced knowledge distillation
  2. Collaborate with cross-functional teams to design and implement enterprise-ready tooling for various training techniques on Amazon SageMaker
  3. Design and execute experiments to optimize model accuracy, latency, and cost across different customization approaches (SFT, DPO, PPO)
  4. Develop and enhance preference learning algorithms and training curricula for customer-specific applications
  5. Create robust evaluation frameworks for assessing model performance across different domains and use cases

Skills

Required

  • building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
  • 1+ years of building machine learning models for business application experience
  • Master's degree, or PhD and 2+ years of applied research experience
  • Experience with any programming language such as Python, Java, C++
  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development
  • PhD in computer science, machine learning, engineering, or related fields, or Master's degree
  • PhD in computer science, computer engineering, or related field, or experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and PyTorch
  • Experience that includes strong analytical skills, attention to detail, and effective communication abilities, or experience in software development and experience in managing and troublshooting network
  • Experience collaborating with cross-functional teams
  • Experience in developing and implementing algorithms and models for supervised fine-tuning and reinforcement learning
  • Experience with patents or publications at top-tier peer-reviewed conferences or journals

What the JD emphasized

  • building models for business application experience
  • patents or publications at top-tier peer-reviewed conferences or journals
  • building machine learning models for business application experience
  • applied research experience
  • deep learning models architecture design and deep learning training and optimization and model pruning
  • developing and implementing algorithms and models for supervised fine-tuning and reinforcement learning
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • customization of Amazon Nova
  • developing advanced customization capabilities
  • enterprise-ready tooling for various training techniques
  • optimize model accuracy, latency, and cost
  • preference learning algorithms and training curricula
  • Responsible AI toolkit
  • secure access mechanisms for early model checkpoints and weights