About the Role
Amazon’s AGI Information is seeking an exceptional Applied Scientist to drive science advancements in the Amazon Knowledge Graph team (AKG). AKG is re-inventing knowledge graphs for the LLM-era, optimizing for LLM grounding and other LLM-based customer experiences. At the same time, AKG is innovating to utilize LLMs in the knowledge graph construction pipelines to overcome obstacles that traditional technologies could not overcome.
Responsibilities
As a member of the AKG Science team, you will have the opportunity to work on interesting problems with immediate customer impact. The team is addressing challenges in automating gap detection, data ingestion, fact verification, query optimization and entity resolution. Your initial focus will be on entity resolution, and you will also have the opportunity to work with scientists working on the other challenges, and with the engineering teams that deliver the science advancements to our customers.
Requirements
A successful candidate has a strong machine learning and LLM background, is a master of state of the art techniques, has a strong publication record, has a desire to push the envelope in one or more of the above areas, and has a track record of delivering to customers. The ideal candidate enjoys operating in dynamic environments, is self-motivated to take on new challenges, and enjoys working with customers, stakeholders and engineering teams to deliver big customer impact, shipping solutions via rapid experimentation and then iterating on user feedback and interactions.
Basic Qualifications
- 3+ years of applied research experience
- Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
- 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
Preferred Qualifications
- Experience with popular deep learning frameworks such as MxNet and TensorFlow.