Microsoft Senior AI Applied Scientist 

Taiwan, Taoyuan City 

896205840

to join our team.

As a, 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.

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.

This rolewill combineAI knowledge withapplied scienceanddemonstrate a growth mindsetand.

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 Computer Science, Statistics, Electrical/Computer Engineering,Physics, Mathematicsor related field AND 4+ years of experience in AI/ML, predictive analytics, or research
• • OR Master’s degree AND 3+ years of experience
  • OR PhDAND 1+ year of experience
  • OR equivalent experience

• 1+ years of experience with generative AI OR LLM/ML algorithms

Other Requirements

• Ability 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, monitoring, and retraining pipelines.

• Familiarity with modernLLMOpsframeworks (e.g.,LangChain,PromptFlow)

• 3+ years of experience publishing in peer-reviewed venues or filing patents

• Experience presenting at conferences or industry events

• 3+ years of experience conducting research in academic or industry settings

• 1+ year of experience developing and deploying live production systems

• 1+ years of experience working with Generative AI models and ML stacks

• Experience across the product lifecycle from ideation to shipping

Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 per year.Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: .

Microsoft will accept applications and processes offers for these roles on an ongoing basis.

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, 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 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 to identify gaps in AI quality, uncover insights and implementPoCsto show proof of concepts.

Leveraging Researchin real-world problems

• deepexpertise in AI subfields (e.g.,deep learning, Generative AI,NLP,muti-modal models)to translate cutting-edge research 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 integrate state-of-the-art research 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 proactively identifying and mitigating ethical and security risks—includingXPIA(Cross-Prompt Injection Attack)unfairness, bias, and privacy concerns—to ensure equitable and 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.

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, classical ML,and optimization 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 and state-of-the-art models, 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.