微软Principal Applied Science Manager
任职要求
Required/Minimum Qualifications: • Undergraduate degree in Computer Science, Engineering, Mathematics, Statistics. • 8+ years development skills in Python, C#, C++ or Java. • 2+ years of people management and/or project management experience. Other Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: • Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Preferred Qualifications • Masters or PhD degree in Computer Science, Statistics, or related fields (undergraduates with significant appropriate experience will be considered). • Strong academic work and professional experience in statistics, machine learning, including deep learning, NLP, econometrics. • Experience in building cloud-scale systems and experience working with open-source stacks for data processing and data science is desirable. • Experience with LLMs in natural language, AI …
工作职责
• Initiate and advance research to advance state-of-the-art in AI for Software Engineering • Collaborate across disciplines with product teams across Microsoft and Github • Stay up to date with the research literature and product advances in AI for software engineering • Collaborate with world renowned experts in programming tools and developer tools to integrate AI across software development stack for Copilot • Build and manage large-scale AI experiments and models.
• Technical Architecture Design: Develop and execute system architecture and technical roadmaps to ensure the system's high availability, scalability, and security. • Cross-Team Collaboration: Work closely with product managers, UX designers, data scientists, and other team members to understand business requirements and translate them into technical solutions. • Continuous Improvement and Optimization: Monitor system performance, optimize performance, and troubleshoot issues to ensure stable and efficient system operation. • Technical Innovation: Stay attuned to industry trends and new technologies, actively promoting innovation and the adoption of best practices. • Quality Assurance: Establish and enforce standards for code reviews, unit testing, and integration testing to ensure high code quality and system reliability.
• 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.
• Research, design, and prototype methods to leverage LLMs for product scenarios such as text understanding, summarization, dialogue, translation, content generation, and reasoning. • Fine-tune, adapt, and optimize pre-trained LLMs for domain-specific tasks while balancing model performance, efficiency, and cost. • Develop scalable pipelines for data collection, cleaning, augmentation, and evaluation. • Collaborate with product and engineering teams to translate applied research into production-quality features. • Define and track key performance metrics for LLM-based features, including accuracy, latency, robustness, and user satisfaction. • Stay current with advances in generative AI, multimodal models, and applied ML techniques, and bring forward innovative ideas to improve our products. • Publish technical insights internally (and externally where appropriate) to advance organizational knowledge and thought leadership.
• Research, design, and prototype methods to leverage LLMs for product scenarios such as text understanding, summarization, dialogue, translation, content generation, and reasoning. • Fine-tune, adapt, and optimize pre-trained LLMs for domain-specific tasks while balancing model performance, efficiency, and cost. • Develop scalable pipelines for data collection, cleaning, augmentation, and evaluation. • Collaborate with product and engineering teams to translate applied research into production-quality features. • Define and track key performance metrics for LLM-based features, including accuracy, latency, robustness, and user satisfaction. • Stay current with advances in generative AI, multimodal models, and applied ML techniques, and bring forward innovative ideas to improve our products. • Publish technical insights internally (and externally where appropriate) to advance organizational knowledge and thought leadership.