微软Principal Applied Scientist--Teams
任职要求
Required Qualifications: • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) • OR equivalent experience. • 5+ years of experience in applied machine learning, natural language processing (NLP), or related domains. • Strong knowledge of modern NLP methods, particularly transformers, LLMs, and transfer learning. • Hands-on experience with at least one deep learning framework (e.g., PyTorch, TensorFlow, JAX). • Proficiency in Python and familiarity with ML tooling, experimentation, and evaluation frameworks. • Experience with data preprocessing, feature engineering, and large-scale training/inference. • Strong analytical and problem-solving skills, with ability to bridge research and product needs. 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 …
工作职责
• 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.
• 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.
• 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.
• 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.
• Design and implement advanced LLM-based architectures and agentic systems for real-world product scenarios.• Translate research breakthroughs into production-ready algorithms, contributing to core capabilities such as reasoning, planning, long-term memory, code-gen based design.• Monitor and improve model performance post-deployment through data-driven iteration and error analysis.• Collaborate across teams to deliver robust, scalable models aligned with product objectives and user value.• Contribute to the organization’s scientific direction by identifying research opportunities that drive long-term differentiation.