Applied Scientist II - Gen AI & Llm, Pxt

Amazon Amazon · Big Tech · Seattle, WA · Data Science

Applied Scientist II role focused on designing, developing, and deploying Generative AI and LLM solutions for Amazon. The role involves working with foundation models, prompt engineering, RAG, fine-tuning, and production deployment of AI systems, with a focus on applied research and evaluation.

What you'd actually do

  1. Design and implement novel GenAI/LLM solutions using foundation models (e.g., Claude, GPT, LLaMA) and AWS services including Amazon Bedrock, SageMaker, and other AWS AI/ML tools
  2. Conduct applied research to advance the state-of-the-art in LLM applications, including prompt engineering, few-shot learning, fine-tuning, and model evaluation
  3. Build scalable, production-ready AI systems that serve millions of requests with high reliability, low latency, and cost efficiency
  4. Partner with product managers, engineers, and business stakeholders to translate business requirements into technical solutions and drive measurable impact
  5. Mentor junior scientists, contribute to technical strategy, and establish best practices for GenAI development across the organization

Skills

Required

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

  • PhD in computer science, machine learning, engineering, or related fields
  • 2+ years of hands-on experience with foundation models and LLM applications (prompt engineering, RAG, fine-tuning, RLHF, Transformer models, and AI Agents)
  • Experience with AWS AI/ML services (Amazon Bedrock, SageMaker, Lambda, etc.)
  • Publications in top-tier conferences (NeurIPS, ICML, ACL, EMNLP, ICLR, COLM, etc.)
  • Experience with model optimization techniques (quantization, distillation, efficient inference)
  • Knowledge of responsible AI practices including bias detection, fairness, and safety evaluation

What the JD emphasized

  • building models for business application experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Publications in top-tier conferences (NeurIPS, ICML, ACL, EMNLP, ICLR, COLM, etc.)

Other signals

  • building scalable, production-ready AI systems
  • design and implement novel GenAI/LLM solutions
  • applied research to advance the state-of-the-art in LLM applications