Microsoft Applied Scientist II 

Taiwan, Taoyuan City 

346371805

As aApplied Scientistfor BIC, you will play a pivotal role in advancing Microsoft's mission to empower every individual and organization on the planet to achieve more. You will contribute to the development and integration ofAI technologies into Microsoft products and services, ensuring they are inclusive, ethical, and impactful.You will collaborate acrossin machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experiences.

AI

We are in an era of unprecedented innovation and openness. As Microsoft continues to lead in AI, we are seeking individuals to help tackle some of the most exciting and meaningful challenges in the field. Our vision is to builda truly open architecture platform that enables users to summon tailored AI agents to drive real-world outcomes.

Applied Scientist IIin shaping the future of AI agents.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Required Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

• 1+ years' of hands onexperience withgenerativeAIOR LLM (Large Language Models)/ML(Machine Learning)
  

Other Requirements:

• to meet Microsoft, customer and/or government security screening requirementsarerequiredfor this role. These requirements include but are not limited to the following specialized security screenings:
• Microsoft Cloud Background Check: This position will berequiredto pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Preferred Qualifications

• Experience withMLOpsWorkflows, including CI/CD (Continuous Integration and Continuous Delivery/Deployment ) , monitoring, and retraining pipelines.
• Familiarity with modernLLMOpsframeworks (e.g.,LangChain,PromptFlow)
• A track recordofpublishing in peer-reviewed venues or filing patents
• Experience presenting at conferences or industry events
• A track record ofconducting research in academic or industry settings
• Hands-onexperience developing and deploying live production systems
• Experience across the product lifecycle from ideation to shipping

Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $131,400 - $215,400 per year.Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:Microsoft will accept applications for the role until October 15, 2025.

Bringing the State of the Art to Products

• Build collaborative relationships with product and business groups to deliver AI-driven impact
• Research and implementstate-of-the-artusing foundation models, prompt engineering, RAG (Retrival Augmented Generation), graphs, multi-agent architectures, as well as classical machine learning techniques.
• Fine-tune foundation models using domain-specificdatasets. -Evaluate model behavior on relevance, bias, hallucination, and response qualityvia offline evaluations, shadow experiments, online experiments, and ROI (Return on Investment) analysis.
• Build rapid AI solution prototypes, contribute to production deployment of these solutions, debug production code, supportMLOps/AIOps.Contribute to papers, patents, and conferencepresentations. -Translate research into production-ready solutionsand measure their impact through A/B testing and telemetrythat addresscustomer needs.
• Ability to use data toidentifygaps in AI quality, uncover insights and implementPoCsto show proof of concepts.

Leveraging Researchin real-world problems

• deepexpertisein AI subfields (e.g.,deep learning, Generative AI,NLP,muti-modal models)to translatecutting-edgeresearch into practical, real-world solutions that drive product innovation and business impact.
• Share insights on industry trends and applied technologies with engineering and product teams.
• Formulate strategic plans that integratestate-of-the-artresearch to meet business goals.

Documentation

• Maintain clear documentation of experiments, results, and methodologies.
• Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing

,and Security

• Apply a deep understanding of fairness and bias in AI by proactivelyidentifyingand mitigating ethical and security risks—includingXPIA(Cross-Prompt Injection Attack)unfairness, bias, and privacy concerns—to ensureequitableand responsible outcomes.
• Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.
• Contribute to internal ethics and privacy policies andensure responsible AIpracticethroughout AI development cyclefrom data collectionto model development, deployment, and monitoring.

• Embody our and

Specialty Responsibilities

• Design, develop, and integrate generative AI solutions usingfoundation models and more.
• Deep understanding ofsmall and large language models architecture, Deep learning, fine tuning techniques, multi-agent architectures, classicalML,andoptimization techniques to adapt out-of-the-box solutions toparticular businessproblems
• Prepare and analyze data for machine learning,identifyingoptimalfeatures and addressing data gaps.
• Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks andstate-of-the-artmodels, open-source libraries, statistical tools, and rigorous metrics
• Address scalability and performance issues using large-scale computing frameworks.
• Monitor model behavior,guide product monitoring andalerting,andadapt to changes in data streams.