Applied Scientist, Aws Applied AI Solutions Core Services

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

This role focuses on developing and productizing AI solutions for enterprise customers within AWS Applied AI Solutions. The scientist will design and implement machine learning systems for diverse applications like video understanding, geospatial optimization, fraud detection, and anomaly detection, creating scalable algorithms and models. They will conduct experiments with LLMs, computer vision, and agentic AI systems, collaborate with engineering teams to integrate science components into production, and establish evaluation frameworks for performance measurement. The role involves working with product teams to frame problems and validate approaches, with opportunities for publication.

What you'd actually do

  1. Develop and productize AI solutions that address complex technical challenges requiring novel approaches beyond off-the-shelf tools
  2. Design and implement machine learning systems for diverse applications including video understanding, geospatial optimization, fraud detection, anomaly detection, and automation
  3. Create scalable algorithms and models that generalize across multiple customer use cases and business problems
  4. Conduct rigorous experimentation with state-of-the-art techniques including large language models, computer vision, federated learning, or physics-based modeling, and agentic AI systems
  5. Collaborate with engineering teams to integrate science components into production systems with measurable customer impact

Skills

Required

  • 3+ years of 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

Nice to have

  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • Experience with large scale distributed systems such as Hadoop, Spark etc.

What the JD emphasized

  • novel approaches beyond off-the-shelf tools
  • productize AI solutions
  • measurable customer impact
  • rigorous experimentation
  • scalable algorithms and models

Other signals

  • Develop and productize AI solutions
  • Design and implement machine learning systems
  • Create scalable algorithms and models
  • Conduct rigorous experimentation with state-of-the-art techniques
  • Integrate science components into production systems
  • Establish evaluation frameworks