Senior Applied Scientist, Delivery Foundation Model

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

Senior Applied Scientist role focused on developing and implementing novel deep learning foundation models that combine multiple modalities (image, video, geospatial) for logistics use cases. The role involves training these models on vast datasets at Amazon scale, optimizing for inference, and collaborating with other teams for production deployment. It also includes guiding technical direction, mentoring junior scientists, and conducting research.

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

  • Python
  • C++
  • SQL
  • Spark
  • Deep Learning
  • Foundation Models
  • Multimodal Learning
  • Large-scale ML training
  • ML inference optimization
  • Production ML deployments

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. 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

  • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 10+ years of industry or academic research experience
  • 5+ years of building machine learning models or developing algorithms for business application experience
  • Strong publication record at top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, RSS, CoRL) OR Demonstrated experience in applying machine learning innovation in industry
  • Experience mentoring junior scientists / engineers.

Other signals

  • foundation models
  • multimodal
  • large-scale training
  • inference at scale
  • production environments