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, combining multiple modalities (image, video, geospatial) for logistics use cases. The role involves training models on large datasets, optimizing for inference at scale, and collaborating with science and engineering teams for production deployments. It requires guiding technical direction, mentoring, and maintaining individual contributions.

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 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
  • Experience in Hive/Spark/Hbase/Yarn, or experience in SQL Server/MySQL and experience that includes strong analytical skills, attention to detail, and effective communication abilities
  • Experience with programming languages such as Python, Java, C++

Nice to have

  • PhD, or PhD and 5+ years of building models for business application experience
  • 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

  • novel deep learning architectures
  • foundation models
  • multitude of modalities
  • image, video, and geospatial data
  • train foundation models
  • infer at Amazon scale
  • production environments
  • novel foundation model architectures
  • multi-task learning
  • scalable and reusable infra
  • model training, evaluation, and inference
  • conception through deployment
  • production systems
  • multimodal training
  • sophisticated modeling strategies
  • model training and evaluation to inference
  • tooling needed to understand and analyze model performance
  • track record of successful production ML deployments
  • large-scale distributed environments for ML training and inference
  • impactful first-author publications at major conferences

Other signals

  • foundation models
  • multimodal
  • large-scale training and inference
  • production deployments