微软Senior Applied Scientist(LLM)
任职要求
Required Qualifications: • Bachelor'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 Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) • OR equivalent experience. • Hands-on experience with Large Language (LLMs), including evaluation techniques, prompt engineering, model fine-tuning. • 3+ years of experience in applied science or machine learning, natural language processing (NLP), or AI systems. • Proficiency in Python, C#, or similar languages. 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: • Exceptional analytical and problem-solving skills with a track record of tacking complex challenges. • Solid communication and presentation abilities, capable of conveying technical concepts to diverse audiences. • Proven experience across the product lifecycle from ideation to shipping. • Publications in leading AI/ML conferences (e.g., NeurIPS, ICML, ACL), demonstrating thought leadership and technical depth. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form. Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
工作职责
• Build benchmarks, evaluation datasets, metrics, and methods to assess and improve the performance and effectiveness of language models and prompts and drive iterative enhancements. • Deliver high-impact analyses that generate actionable insights to steer product and business decisions and improve user satisfaction. • Collaborate closely with engineering, research, product, and other teams to ensure AI-driven experiences meet quality and user experience standards. • Analyze latest AI innovations and explore opportunities to apply cutting-edge techniques for building scalable, high-impact solutions that enhance product capabilities and deliver exceptional user experiences.
• Design and implement advanced LLM-based architectures and agentic systems for real-world product scenarios.• Lead model training and evaluation efforts, including data preprocessing, fine-tuning, and inference optimization.• Collaborate across teams to deliver robust, scalable models aligned with product objectives and user value.• Apply and adapt research ideas to solve practical challenges in reasoning, planning, memory, and alignment.• Monitor and improve model performance post-deployment through data-driven iteration and error analysis.• Contribute to technical discussions, model reviews, and best practices within the applied science community.
Design, training/evaluation, development/deployment of advanced models (LLM/MLLM/SLM) for ads content creation, ads performance optimization, and automated customer support. Apply strong problem-solving and data analysis skills to identify opportunities, optimize performance, and deliver measurable business impact.Stay up to date with the latest advancements in generative AI, LLM-driven agent, online advertisement, and make integration and improvement for our systems.Develop scalable and robust algorithms and pipelines for efficient model training, data processing, online serving and measurement.Collaborate with cross-functional teams, such as product managers and Infra/UI engineers, to define strategies and deliver solutions that enhance our systems.Present problems, insights, technical solutions, and results clearly to both technical and non-technical stakeholders.
The primary responsibilities will include:• Algorithm Development and Enhancement for ranking algorithms 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.
• Drive data exploration and analysis by collecting initial datasets, selecting appropriate analytical techniques, and applying foundational statistical methods to extract insights. • Build and evaluate ML models by running modeling tools on prepared datasets, analyzing performance, and incorporating customer feedback to improve outcomes. • Contribute to AI development by writing production-quality code for features or models, applying debugging best practices, and staying current with industry trends and methodologies. • Champion customer-centric solutions by understanding business goals, identifying growth opportunities, and managing expectations throughout the project lifecycle. • Collaborate cross-functionally with engineering and product teams to define success metrics, improve AI quality at scale, and shape how performance is measured across Copilot technologies.