Drimva

Amazon

Applied Scientist -- Foundation Models, SSO

$172K–$222K

Santa Clara, CA · Full-time · Posted Aug 17, 2026

Experience
3+ years building models for business application; PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
Education
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience. A PhD in machine learning, computer science, electrical engineering, or a related field is preferred.

About the job

Develop and optimize foundation models and systems for efficient, reliable long-horizon agentic workloads, including planning, tool use, and multi-step task execution.

What you'll do

  • Design, train, fine-tune, evaluate, and improve custom foundation models.
  • Optimize training and inference; profile and address compute, memory, communication, and distributed-training bottlenecks.
  • Develop evaluations for model reliability and long-horizon agent performance.

What they ask for

  • Strong background in machine learning, deep learning, transformers, and foundation models; experience with PyTorch, JAX, or similar frameworks.
  • 3+ years of building models for business application experience.
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
  • Experience with patents or publications at top-tier peer-reviewed conferences or journals; programming experience in Java, C++, Python or related language; experience in at least one listed area such as parallel and distributed computing or high-performance computing.

Nice to have

  • Experience using Unix/Linux and in professional software development.
  • Experience with AWS Trainium, Inferentia, Neuron SDK, TPUs, or other custom accelerators; PhD in a related field.

Pay

171,600.00 - 222,200.00 USD annually (base salary)

Tools

PyTorch, JAX, CUDA, Triton, AWS Trainium, Inferentia, Neuron SDK, TPUs

AI focus

Build foundation models for long-horizon agentic workloads, supporting planning, tool use, memory, self-correction, and multi-step execution; improve model efficiency, stability, and reliability.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 5, 2026 (UTC)