Applied Researcher - Coreai 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.

Minimum Qualifications:

• Bachelor's Degree in Statistics, Mathematics, Computer Science or related field.
• OR 3+ years experience in software development lifecycle, large-scale computing, modeling, cybersecurity, and/or anomaly detection.
• 3+ years of software development/research experience utilizing knowledge of security/threat landscape Practical Windows internals knowledge (other OS experience may count).
• 1+ years of experience with Reverse Engineering: familiar with debuggers, disassemblers, protocols, file formats.

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

Preferred Qualifications:

• Master's Degree in Statistics, Mathematics, Computer Science or related field.
• OR 4+ years experience in software development lifecycle, large-scale computing, modeling, cyber-security, and/or anomaly detection.
• Offensive security research experience.
• Digital forensics skills Programming skills in C, C++, C# or willingness to learn Industry recognized author of security research papers, blogs, or books Low-level/security knowledge of other operating systems.

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 August 21, 2025.

As a Security Researcher II, you will:

• Research emerging attack vectors and techniques.
• Analyze detection and prevention gaps and understand root cause.
• Design and develop detection, prevention and disruption solutions to thwart advanced attacks.
• Synthesize in-field telemetry to judge the state of threat coverage and share insights.
• Identify trends, foresee landscape direction and propose enhancements to meet the needs.
• Collaborate across teams to address systemic security issues.
• Guide design of AI based solutions to tackle hard security problems.

Required/Minimum Qualifications

• Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C#, Java, C++, Go
  • OR equivalent experience.
• 4+ years of experience managing software engineering teams
• 4+ years of experience with distributed systems, microservices, and container orchestration technologies (e.g., Kubernetes, Docker)
• 4+ years of experience with CI/CD pipelines and developer tooling (e.g., GitHub, Azure DevOps)
• 4+ years of experience building and maintaining infrastructure platforms and product APIs with a focus on usability, extensibility, scale and performance

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/Additional Qualifications

• Experience leading teams building infrastructure platforms, APIs, or orchestration frameworks
• Understanding of cloud-native architecture, asynchronous workflows, and service mesh technologies
• Familiarity with agent-based systems, workflow engines, or declarative programming models
• Ability to drive clarity in ambiguous, early-stage platform development
• Experience collaborating with product and design to shape abstractions and reference implementations
• Mentorship skills and a track record of fostering technical excellence and inclusive team culture
• Experience in AI/ML infrastructure, inferencing systems, or high-scale platform engineering
• Hands-on experience with Microsoft Azure services such as AKS, Azure Functions, and Event Grid, CosmosDb
• Demonstrated experience driving AI usage and adoption in platform or product development, including integrating AI capabilities into services or enabling AI-powered developer experiences

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• Lead and grow a team of software engineers focused on building scalable, cloud-native inferencing services and orchestration layers.
• Partner with product, research, and platform teams to define requirements and deliver robust, extensible infrastructure platforms and product APIs.
• Drive architectural decisions and technical strategy for inferencing control systems, ensuring high availability, performance, and cost efficiency.
• Guide the team in designing and implementing distributed systems that support dynamic routing, load balancing, and execution of AI workloads.
• Ensure engineering excellence through code reviews, design reviews, and mentoring, fostering a culture of innovation and inclusion.
• Own the operational health of services, including on-call rotations, incident response, and continuous improvement of reliability metrics.
• Collaborate across CoreAI and Microsoft to align on platform integration, security, and compliance requirements.
• Champion developer experience and usability in SDKs and tooling, enabling seamless integration with agentic and generative AI platforms.

You will be responsible for the design, development, and execution of an exciting research agenda in collaboration with other machine learning, engineers, clinicians, social scientists, and designers at Health Futures. To learn more about this opportunity, please visit:

Qualifications

• PhD in Machine Learning, Computer Science or related fields or equivalent experience.

Required

• Relevant years of experience working on a multidisciplinary team working on AI research for real world impact.
  
• Hands-on experience with large scale deep learning models and libraries (e.g., PyTorch, TensorFlow).
  
• Strong software development skills.

Preferred

• Publications at top conferences and journals such as: NeurIPS, ICML, ICLR, MICCAI, Nature, CVPR, ICCV, EMNLP, ACL.
  
• Experience with medical domains such as radiology, digital pathology, genetics, immunology.
  
• Machine learning expertise in multi-modal learning, large language models (e.g., alignment), reinforcement learning and/or domain adaptation and data-efficient learning.

The ideal candidate will have a strong intellectual curiosity and passion to solve real-world problems in healthcare and multi-modal AI The responsibilities will include:

• Advance multi-modal medical AI, empowering internal and external partners to build and deploy state of the art medical imaging AI to make clinical work-flows faster and safer and improve patient outcomes.
• Collaborate on design, implementation and evaluation of multi-modal machine learning solutions which consider key clinical factors and responsible AI.
• Support the strategic planning of the team by providing engineering and research leadership.
• Engage with external and internal collaborators to drive real world impact.