Amazon's AGI Web & Knowledge Services group is seeking a passionate, talented, and inventive Applied Scientist to lead the development of industry-leading structured Information retrieval systems. As part of our cutting-edge AGI-SIR team, you will play a pivotal role in developing efficient AI solutions for Knowledge Graphs, Graph Search and Question Answering Systems.
In this role, your work will focus on creating scalable and efficient AI-driven technologies that push the boundaries of information retrieval. You will work on a broad range of problems, from low-level data processing to the development of novel retrieval models, leveraging state-of-the-art machine learning methods.
Key job responsibilities
- Lead the development of advanced algorithms for knowledge graphs, graph search and question answering systems, guiding the team in solving complex problems and setting technical direction.
- Design models that address customer needs, making informed trade-offs to balance accuracy, efficiency, and user experience.
- Collaborate with engineering teams to implement successful models into scalable, reliable Amazon production systems.
- Present results to technical and business audiences, ensuring clarity, statistical rigor, and relevance to business goals.
- Establish and uphold high scientific and engineering standards, driving best practices across the team.
- Promote a culture of experimentation and continuous learning within Amazon’s applied science community.
In this role, your work will focus on creating scalable and efficient AI-driven technologies that push the boundaries of information retrieval. You will work on a broad range of problems, from low-level data processing to the development of novel retrieval models, leveraging state-of-the-art machine learning methods.
Key job responsibilities
- Lead the development of advanced algorithms for knowledge graphs, graph search and question answering systems, guiding the team in solving complex problems and setting technical direction.
- Design models that address customer needs, making informed trade-offs to balance accuracy, efficiency, and user experience.
- Collaborate with engineering teams to implement successful models into scalable, reliable Amazon production systems.
- Present results to technical and business audiences, ensuring clarity, statistical rigor, and relevance to business goals.
- Establish and uphold high scientific and engineering standards, driving best practices across the team.
- Promote a culture of experimentation and continuous learning within Amazon’s applied science community.