Senior Applied Scientist, New Initiatives

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

Senior Applied Scientist role focused on building agentic AI systems, multi-agent architectures, tool-augmented LLMs, and RAG pipelines for climate-related products. The role involves end-to-end product development from research to production, with a focus on autonomous analysis, planning, and execution of recommendations, leveraging multimodal AI and deep learning on time series data.

What you'd actually do

  1. design and deploy agentic AI systems that autonomously analyze data, plan solutions, and execute recommendations
  2. build multi-agent architectures where specialized AI agents coordinate to solve complex optimization problems
  3. develop tool-augmented LLM applications that integrate with external data sources and APIs to deliver context-aware insights
  4. creating multimodal AI systems that synthesize diverse data streams, while implementing RAG pipelines that ground large language models in domain-specific knowledge bases
  5. drive end-to-end product development from research through production deployment, collaborating with cross-functional teams to translate AI capabilities into customer experiences

Skills

Required

  • building machine learning models for business application experience
  • PhD, or Master's degree and 6+ years of applied research experience
  • Experience programming in Java, C++, Python or related language
  • Experience with neural deep learning methods and machine learning
  • Experience with large language models (LLMs) and generative AI applications

Nice to have

  • Experience with EnergyPlus simulator or other building energy simulation tools
  • Knowledge of IoT systems, real-time data processing, and edge computing
  • Experience in energy domain applications such as load forecasting, consumption analysis, or smart home optimization
  • Track record of innovation through patents, open-source contributions, or publications in top-tier conferences and journals

What the JD emphasized

  • agentic AI applications
  • autonomous AI agents that can reason, plan, and execute multi-step tasks
  • tool-augmented LLM systems
  • multi-agent orchestration
  • RAG architectures

Other signals

  • building products that have a meaningful impact for customers and the climate
  • design and deploy agentic AI systems that autonomously analyze data, plan solutions, and execute recommendations
  • build multi-agent architectures where specialized AI agents coordinate to solve complex optimization problems
  • develop tool-augmented LLM applications that integrate with external data sources and APIs to deliver context-aware insights
  • creating multimodal AI systems that synthesize diverse data streams
  • implementing RAG pipelines that ground large language models in domain-specific knowledge bases
  • apply advanced machine learning and deep learning techniques to time series analysis, forecasting, and pattern recognition
  • drive end-to-end product development from research through production deployment
  • establish rigorous experimentation frameworks to validate model performance and measure business impact