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.BASIC QUALIFICATIONS- Experience programming in Java, C++, Python or related language- PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience- Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)- 1+ years of building models for business application experience ...

Amazon’s AGI Information is seeking an exceptional Applied Scientist to develop 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.As a member of the AKG Science team, you will have the opportunity to work on interesting cutting edge problems with immediate customer impact. The team is addressing challenges in automating gap detection, data ingestion, fact verification, query optimization and entity resolution. You will work with the entity resolution team to develop generic entity resolution models that work for every class of data in AKG (e.g., people, places, movies, etc.), that scale to hundreds of millions of entities, and that work incrementally. You will collaborate with other scientists to develop entity representations, training data and matching models to solve the problem. You will also work to optimize models to be performant and cost-effective in production.A successful candidate has a strong machine learning and LLM background, identifies and adapts state of the art techniques to solve new problems, builds POCs quickly to identify promising techniques and reject ideas that do not work, and enjoys collaborating with engineering teams to ship solutions incrementally via rapid experimentation. A successful candidate is an avid reader of the scientific literature and works to publish in peer reviewed conferences.Key job responsibilitiesAs an Applied Scientist, you will apply scientific rigor to your work. You will work incrementally, setting up and executing experiments informed by rigorous failure space analysis of prior experiments. You have a knack for writing clear, succinct and informative reports of your experiments. You collaborate and seek guidance from other scientists on your team to define the next experiments. You work with engineers to understand deployment requirements and constraints, and work to address them. You work continuously with customers and stakeholders to simplify and adapt to deliver for our customers.BASIC QUALIFICATIONS- 3+ years of building models for business application experience- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience- 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 ...

The Amazon Artificial General Intelligence (AGI) Personalization team is looking for a passionate, highly skilled and inventive Applied Scientist with strong machine learning background to build state-of-the-art ML systems for personalizing large-scale, high-quality conversational assistant systems. As a Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graph, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences.Key job responsibilities- Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals- Innovate new methods for contextual knowledge extraction and information retrieval, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, compute, latency and quality- Research in advanced customer understanding and behavior modeling techniques- Collaborate with cross-functional teams of scientists, engineers, and product managers to identify and solve complex problems in personal knowledge aggregation, processing, modeling, and verification- Design and execute experiments to evaluate the performance of state-of-the-art algorithms and models, and iterate quickly to improve results- Think Big on conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems- Communicate results and insights to both technical and non-technical audiences, including through presentations and written reportsAbout the teamThe AGI Personalization org uses various contextual signals to personalize Large Language Model output for our customers while maintaining privacy and security of customer data. We work across multiple Amazon products, including Alexa, to enhance the user experience by bringing more personal context and relevance to customer interactions.BASIC QUALIFICATIONS- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience- 2+ 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 ...

The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Applied Scientist, to support the development and implementation of cutting-edge algorithms and models for supervised fine-tuning; with a focus across text, image, and video modalities.As an Applied Scientist, you will play a critical role in supporting the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences.Key job responsibilities- Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI- Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results- Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems- Communicate results and insights to both technical and non-technical audiences, including through presentations and written reportsBASIC QUALIFICATIONS- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience- 1+ years of building models for business application experience- Experience programming in Java, C++, Python or related language- Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF ...