亚马逊Applied Scientist, Generative AI Innovation Center

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. Starting in 2024, the Innovation Center launched a new Custom Model and Optimization program to help customers develop and scale highly customized generative AI solutions. The team helps customers imagine and scope bespoke use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop and optimize models to power their solutions, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. As an Applied Scientist, you will - Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges - Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production - Help customers optimize their solutions through approaches such as model selection, training or tuning, right-sizing, distillation, and hardware optimization - Provide customer and market feedback to product and engineering teams to help define product direction

LLM Application in Customer Service: Design, develop, and iterate on prompts for various LLM applications within the customer service center, including conversational AI, content generation, and summarization. Experiment with prompt formats, styles, and techniques to optimize LLM performance and output quality. Analyze LLM-generated responses, identify biases or limitations, and implement mitigation strategies to enhance customer satisfaction and service performance. Operational Excellence: Identify and propose innovative projects that leverage LLMs to solve complex problems or explore new capabilities in customer service. Conduct research, experiment with cutting-edge techniques, and develop prototypes for potential products or services. Collaborate with data scientists, machine learning engineers, and other stakeholders to integrate prompts and research findings into the broader AI/ML pipeline. Performance Analysis: Analyze the content of customer interactions to generate self-service rate analysis reports, explaining why customers transfer to agents and what can be done to improve these indicators. Generate regular chat-bot performance reports and continuously improve the algorithm model accuracy, customer satisfaction, and service performance of the chat-bot. Team Collaboration: Work closely with relevant function teams to deliver an integrated workflow of demand negotiations, standard creations, and project implementations. Share knowledge and best practices with the team, mentor junior AI operation peer, and contribute to a collaborative learning environment.

  • Algorithm Development and Enhancement for Content Quality in News & Feeds• Work with cross-functional teams to design, develop, and implement recommendation algorithms to deliver product features and drive user engagement.- Optimize existing recommendation algorithms by analyzing performance metrics and user feedback, incorporating advanced machine learning techniques including generative AI techniques. • Innovation in the area of NLP, LLM, and recommender system. • Data Analysis and Modeling• Perform data analysis to identify patterns, trends, and opportunities to improve the relevance and quality of our recommendation systems. • Build systemic solutions and models to optimize user experience.

• Develop next-generation AI experiences for Microsoft Edge — leverage machine learning and generative AI to reinvent how users browse, search, and interact with the web. • Advance Edge’s contextual intelligence by building models that synthesize browsing history, page content, and user intent to deliver proactive, personalized, and trustworthy assistance. • Drive innovation in agentic systems, prototyping and productionizing conversational, reasoning, and planning models that transform Edge from a static browser into a true AI companion. • Collaborate cross-functionally with product managers, designers, and engineers to translate AI capabilities into elegant, high-utility user experiences. • Own the full AI feature development lifecycle — from data pipeline and evaluation metric design to model and prompt tuning, quality validation, and continuous improvement.