Principal Applied Scientist, Delivery Foundation Model

Amazon Amazon · Big Tech · Bellevue, WA · Applied Science

Principal Applied Scientist role focused on developing and implementing novel foundation models for Amazon's delivery logistics. The role involves designing deep learning architectures, training models on vast datasets, and ensuring production-level performance for multimodal data (image, video, geospatial). It emphasizes scientific leadership, collaboration, and mentoring, with a strong focus on both research and engineering aspects of foundation model development and deployment at scale.

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. Guide technical direction for specific research initiatives, ensuring robust performance in production environments.
  5. Mentor fellow scientists while maintaining strong individual technical contributions.

Skills

Required

  • PhD or Master's degree and 8+ years of applied machine learning experience
  • Designing novel deep learning model architectures
  • Building models from scratch
  • Proficient with Data, experience with SQL and Spark
  • Expert coders comfortable working in production environments using Python, C++ or other languages
  • Strong publication record at top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, RSS, CoRL) OR Demonstrated experience in applying machine learning innovation in industry
  • Extensive track record of leading technical projects
  • Experience mentoring junior scientists / engineers

Nice to have

  • Experience building foundation models for industry or research
  • Experience designing multi-modal model architectures
  • Experience building models for motion prediction, e.g. for autonomous driving
  • Track record of successful production ML deployments
  • Experience with large-scale distributed environments for ML training and inference
  • History of impactful first-author publications at major conferences

What the JD emphasized

  • novel deep learning architectures
  • foundation models
  • vast amounts of Amazon data
  • infer at Amazon scale
  • production environments
  • foundation model developer
  • multimodal training
  • model training and evaluation to inference
  • large-scale distributed environments for ML training and inference

Other signals

  • foundation models
  • multimodal
  • large-scale training
  • inference at scale
  • logistics
  • delivery