Applied Scientist jobs at Microsoft in Taiwan, Taoyuan City

and multi-disciplinary group of scientists,

Principal Data & Applied Scienwork ongroundbreaking andhigh-impact projects at the intersection of AI and software engineering. In this role, you will drive the development and application of advanced data science techniques—especially those involving LLMs—to real-world developer workflows.. You will have the opportunity to design and lead experiments, build and evaluatemodels (including RAG pipelinesand evaluation frameworks), andinsights into scalable product features. Most importantly,play a key role in bridging

Required/Minimum Qualifications:

• degree in Computer Science, Engineering, Mathematics, Statistics

• +yearsexperiencewith strongproficiencyinPython, machine learning frameworks,and experience developing and deploying generative AI or machine learning models into production. (Experience withadditionalprogramming languages is a plus but notrequired

Preferred Qualifications

• or PhD degree in Computer Science, Statistics, or related fields (undergraduates with significantappropriate experiencewill be considered).

• Strong 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 for code or related fields

• Excellent communication skills, ability to present andwritereports, strongteamworkand collaboration skills.

• Experience in productizing AI and collaborating with multidisciplinary teams.

• researchand productization ofstate-of-the-art in AI for Software Engineering

• Build and manage large-scale AI experiments and models.

• Drive experimentation through A/B testing and offline validation to evaluate model performance.

• and geographieswith product teamsinMicrosoft 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

• Lead others by exemplifying growth mindset, inclusiveness, teamwork, and customer obsession

The Security Models Training team builds and operates the large-scale AI training and adaptation engines that power Microsoft Security products, turning cutting-edge research into dependable, production-ready capabilities. As a, you will lead end-to-end model development for security scenarios, including privacy-aware data curation, continual pretraining, task-focused fine-tuning, reinforcement learning, and rigorous evaluation. You will drive training efficiency on distributed GPU systems, deepen model reasoning and tool-use skills, and embed responsible AI and compliance into every stage of the workflow. The role is hands-on and impact-focused, partnering closely with engineering and product to translate innovations into shipped experiences, designing objective benchmarks and quality gates, and mentoring scientists and engineers to scale results across globally distributed teams. You will combine strong coding and experimentation with a systems mindset to accelerate iteration cycles, improve throughput and reliability, and help shape the next generation of secure, trustworthy AI for our customers.

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ 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 6+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 3+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
• Proven track record training and shipping large language models or multimodal models for production scenarios, including continual pretraining and task specific adaptation.
• Proficiency in Python and PyTorch, with hands-on experience building and debugging large-scale training jobs.

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 background and Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Security domain experience in one or more areas: security operations, threat intelligence, malware analysis, vulnerability and posture management, anomaly detection, phishing and fraud detection, or cloud identity and access.
• Experience with distributed training and scaling techniques, for example DeepSpeed, FSDP, ZeRO, model and pipeline parallelism, mixed precision, and profiling.
• Experience with privacy preserving ML including differential privacy concepts, privacy risk assessment, and utility measurement on privatized data.

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 21, 2025.

• Lead the full modeling lifecycle for security scenarios, from data ingestion and curation to training, evaluation, deployment, and monitoring.
• Architect and optimize large-scale distributed training on GPU clusters, improving throughput, reliability, and cost efficiency.
• Design and implement privacy-preserving data workflows, including anonymization, templating, synthetic augmentation, and quantitative utility measurement.
• Develop and maintain fine-tuning and adaptation recipes for transformer models, including parameter-efficient methods and reinforcement learning from human or synthetic feedback.
• Establish objective benchmarks, metrics, and automated gates for accuracy, robustness, safety, and performance, enabling repeatable model shipping.
• Collaborate with engineering and product teams to productionize models, harden pipelines, and meet service-level objectives for latency, throughput, and availability.
• Mentor applied scientists and engineers, promote strong documentation and experiment hygiene, and foster a culture of rapid iteration grounded in responsible AI principles.
• Contribute to thought leadership by staying current with the latest AI advances and translating promising techniques into practical, measurable impact.

? The Business and Industry Solutions (BIS) team is looking for a Senior Applied Scientist to drive innovation at the intersection of AI, experimentation, and enterprise systems. In this role, you will design and evaluate autonomous agents that deliver measurable improvements in accuracy, latency, and cost-efficiency.lead rapid experimentation cycles, develop robust evaluation frameworks, and apply advanced techniques like reinforcement learning to enable multi-step reasoning and decision-making.collaborate across engineering, product, and partner teams to ensure agents are performant, secure, reliable, and extensible—empowering customers and partners to build on our platform. This is your opportunity to influence the next generation of AI-native business applications and deliver real-world impact at scale.

in natural language processing (NLP), witha strong foundationin large language model (LLM) development, evaluation, and fine-tuning. They should have hands-on experience in applying advanced fine-tuning techniques—including instruction tuning, reinforcement learning from human feedback (RLHF), and tool-augmented generation—to build agents capable of multi-step reasoning and decision-making. Familiarity with prompt/context engineering, context-aware orchestration, and integrating LLMs with external tools and APIs is essential. The candidate should be comfortable working in a fast-paced, experimentation-driven environment,both offline and online evaluation methods to iterate rapidly andagent behavior. A deep understanding of the challenges and opportunities in building AI-native enterprise applications will be key to success in this role.

Qualifications

• Bachelor's Degree in 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 Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

• OR Doctorate in Computer Science, Electrical or Computer Engineering, or related field AND 1+ years related experience (e.g., statistics, predictive analytics, research)

• OR equivalent experience.
• 1+ years of experience withgenerativeAIOR LLM/ML

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

and/or government security screening requirementsfor this role. These requirements include but are not limited to the following specialized security screenings:

• requiredto pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

• by executing high‑leverage data science and analytics initiatives within a product area or feature team, ensuring measurable improvements to user and business outcomes.

• Lead the design and implementationof advanced model fine‑tuning pipelines, including Reinforcement Learning from Human Feedback (RLHF), to align AI system behavior with user intent and improve performance in real‑world scenarios.

• Own complex, end‑to‑end projectsthat combine technical depth with cross‑functional collaboration, influencing feature direction and prioritization rather than broad organizational investment decisions.

• Foster alignment and trustacross partner teams through clear, actionable communication and collaborative problem‑solving.

• Develop and maintainrobust measurement systems, experimentation frameworks, and causal inference methodologies tailored to dynamic AI systems and enterprise‑scale environments.

• Mentor and supportpeers by sharing best practices, reviewing designs, and contributing to a collaborative, high‑performance team culture.

• Leverage AI tostreamline workflows and enhance team productivity through intelligent automation and innovation.

As aScience Managerfor 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 ofYou will collaborate acrossThis rolewill combineAI knowledge withapplied sciencedemonstrate a growth mindset

Required Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ 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 6+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• 3+ years of people management experience.
• 1+ years of experience withgenerativeAIOR LLM/ML

Other Requirements:

• to meet Microsoft,customerand/or government security screening requirementsarerequiredfor this role. These requirements include but are not limited to the following specialized security screenings:

• requiredto 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)

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

• Experience presenting at conferences or industry events

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

• + year of experience developing and deploying live production systems

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

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
• LeadResearch and implementation ofstate-of-the-artusing foundation models, prompt engineering, RAG, graphs, multi-agent architectures, as well as classical machine learning techniques.
• Fine-tunefoundation 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 conference

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-art

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

• Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.
• Contribute to internal ethics and privacy policies andensure responsible AIpracticethroughoutAIdevelopment 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, classicalML,andoptimization techniques to adapt out-of-the-box solutions toparticular businessproblems
• Prepare and analyze data for machine learning,identifyingoptimalfeaturesandaddressingdata gaps.

high energyengineers to help achieve that mission.

data across the full Azure stack, HW & SW components, Silicon development processes, and much more. CHAT Data Science& Engineeringscience, and

We further usethat datato power AI solutions across the SCHIE orgdesigned to improve efficiency,

Required/minimum qualifications

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• 7+ years industry experience
• 1+ years ofhands onexperience working with AI solutions in the industry

Other Qualifications:

• Abilityto meet Microsoft, customer and/or government security screening requirementsarerequired 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.

Additional or preferred qualifications

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

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 17th, 2025.

• Identify opportunities to leverage both custom-built and existing AI solutions to drive business impact across the division.

• Design and develop complex software systems, ensuring scalability, performance, and maintainability.

• Architect and implement AI-driven solutions to solve real-world business challenges.

• Stay current with emerging trends, tools, and best practices in AI and related technologies.

• Collaborate cross-functionally with teams in a dynamic, fast-paced environment to deliver high-impact solutions.

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+ 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.
• 2+ years of applied science or data science 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:

• 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 2+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 2+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).

• 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.

You will collaborate across product,

This role will combine AI knowledge with applied scienceexpertise, and

Required Qualifications

• Bachelor’s degree in computer science, Statistics, Electrical/Computer Engineering, Physics, Mathematics or related field AND experience in AI/ML, predictive analytics, or research- OR Master’s degree- OR PhD- OR equivalent experience

• Experience with generative AI OR LLM/ML algorithms

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:

• requiredto 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

• Experience developing and deploying live production systemsone or more of the following: C#, Java, React/Angular, TypeScript.

• Experience with design and implementation of enterprise-scale services

• Experience publishing in peer-reviewed venues or filing patents

• Experience presenting at conferences or industry events

• Experience conducting research in academic or industry settings

• Experience working with Generative AI models and ML stacks

• Experience across the product lifecycle from ideation to shipping

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-specific datasets. - Evaluate model behavior on relevance, bias, hallucination, and response quality via 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 conference presentations. - Translate research into production-ready solutions and measure their impact through A/B testing and telemetry that address customer needs.
• Ability to use data toidentifygaps in AI quality, uncover insights and implementPoCsto show proof of concepts.

Leveraging Research in real-world problems

• Demonstrate 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-art

Documentation

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

Apply a deep understanding of fairness and bias in AI by proactivelyand responsible outcomes.

• Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.
• Contribute to internal ethics and privacy policies and ensure responsible AI practice throughout AI development cycle from data collection to model development, deployment, and monitoring.

Specialty Responsibilities

• Design, develop, and integrate generative AI solutions using foundation models and more.
• Deep understanding of small 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 and alerting, and adapt to changes in data streams.