Applied Scientist (genai/llm), Sandstone

Amazon Amazon · Big Tech · San Diego, CA · Machine Learning Science

This role focuses on researching and implementing generative AI algorithms and foundational behavioral models for Amazon Stores, using LLMs and large model training techniques. The scientist will optimize model performance for inference and deployment, collaborate on data preprocessing and training infrastructure, experiment with SOTA methods, and provide technical expertise for integrating AI solutions into products. Requires a PhD or Master's with significant experience in ML/AI, building models for business applications, and programming skills, with a preference for experience with PyTorch, large datasets, NLP, and computer vision.

What you'd actually do

  1. Research and implement new algorithms and architectures for generative AI applications.
  2. Optimize model performance and scalability for inference and deployment.
  3. Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation.
  4. Experiment with SOTA methods to improve generative AI model quality.
  5. Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 2+ years of building models for business application experience
  • 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
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals

Nice to have

  • Strong understanding of machine learning, deep learning, and generative AI principles and algorithms.
  • Proficiency with modeling and large-scale data processing tools such as Pytorch, scikit-learn, Spark MLLib, PySpark, MxNet, Tensorflow, numpy, scipy etc.
  • Ability to work with large datasets and knowledge of data preprocessing techniques such as Hadoop, Spark etc..
  • Familiarity with natural language processing (NLP) and computer vision for generative AI applications.
  • Strong problem-solving and critical thinking skills and excellent communication and teamwork abilities to collaborate with cross-functional teams.
  • Ability to stay updated with the latest advancements in generative AI and adapt to new techniques and methodologies.

What the JD emphasized

  • building foundational behavioral models
  • Large Model training techniques
  • rigorous testing
  • successful deployment
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • building foundational behavioral models
  • Generative AI, LLMs and Large Model training techniques
  • fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge
  • state-of-the-art Generative AI algorithms