Applied Scientist, Delivery Foundation Model

Amazon Amazon · Big Tech · Santa Clara, CA · Applied Science

Applied Scientist role focused on developing and implementing novel foundation models for logistics, involving multimodal data, training at scale, and inference. The role spans from data preparation to model training, evaluation, and inference, with a focus on production environments.

What you'd actually do

  1. Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data.
  2. Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries.
  3. As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network.
  4. Drive technical direction for specific research initiatives, ensuring robust performance in production environments.
  5. Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure

Skills

Required

  • PhD, or Master's degree and 1+ years of CS, CE, ML or related field experience
  • 4+ years of programming in Java, C++, Python or related language experience
  • Experience building machine learning models or developing algorithms for business application
  • 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

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • foundation models
  • multimodal
  • Amazon scale
  • production environments
  • model training
  • model evaluation
  • inference

Other signals

  • foundation models
  • multimodal
  • Amazon scale
  • logistics