Research 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.

is committed to advancing cutting-edge AI technologies that enable deeper understanding and interaction with people, objects, and environments in the 3D real world. Our research spans a diverse range of areas, including computer vision, generative AI, 3D perception, and robotic action learning, among others relevant to Embodied AI. By pushing the boundaries of these domains, we aim to develop innovative solutions that bridge the gap between the digital and physical worlds, empowering AI systems to perceive, comprehend, and navigate complex real-world scenarios.

Required Qualifications:

• Currently enrolled in a PhD program in Robotics, Machine Learning, Computer Science, or a related STEM field.
• Research experience inembodied AI, robotics, AI models, natural language, computer vision, demonstrated for example through research in a related PhD program and/or publications in conferences or scientific journals.
• Hands-on experience with Python and modern deep learning frameworks.
• Excellent problem-solving skills and the ability to work independently as part of a team.
• Strong communication skills and the ability to present complex ideas clearly.

Other Requirements:

• Ability to physically work from Microsoft Research Asia – Tokyo (Japan) for the duration of the internship.
• Must obtain permission from your academic advisor and commit to at least four months of internship.

Preferred/Additional Qualifications:

• Proven software engineering skills, evidenced by professional experience, internships, and impactful open-source contributions.
• Practical experience with handling data and robot learning, such as experience in Vision-Language-Action modelsor Hand Object Interactions.
• Familiarity with1)robot learning for robot hand manipulationor 2) hand pose estimation techniques or 3) reasoning techniques (Chain-of-Thought) used in LLM.

• Contribute to a high-impact research agenda within the context of a highly collaborative research culture alongside a team of experts in Embodied AI.
• Design and implement experiments to test new hypotheses and validate research findings.
• Communicate research findings to an interdisciplinary research team.
• Prepare technical papers, presentations, and open-source releases of research code.

At , we believe machine learning and artificial intelligence has the potential to transform scientific modelling and discovery crucial for solving the most pressing problems facing society including sustainable materials and discovery of new drugs.

Required:

• PhD or equivalent experience in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or related field.
• Strong software engineering in Python (packaging, testing, CI), with systems thinking for data‑intensive ML.
• Deep learning experience (PyTorch/JAX/TensorFlow) and solid foundations in linear algebra, probability, and statistics.
• Proven experience designing robust data pipelines for large‑scale ML (HPC or cloud).
• Ability to reason about learning signal and to assess information content of real‑world scientific datasets.
• Excellent collaboration and communication in interdisciplinary teams.

Preferred:

• Hands‑on cryo‑EM experience (e.g., map reconstruction, refinement, or pipeline tooling).
• CUDA or C++ for performance‑critical components; experience with mixed precision and memory‑efficient training.
• Experience integrating experimental data into ML models (e.g., constraints/priors from cryo‑EM, binding assays, spectroscopy).
• Familiarity with MD data, structure prediction systems, or protein design work-flows.
• Experience with cost‑optimization for data collection and cloud utilization; clear track record of building reliable, maintainable research software at scale.
• Experience with structural biology or molecular biology data/techniques (e.g., cryo‑EM, binding assays, spectroscopy, expression, sequencing)

• Data integration for structure & dynamics: Build ingestion/curation pipelines for structural/biophysical data (mmCIF/PDB, EM maps/particles, binding/biophysics, spectroscopy); implement map/volume preprocessing (e.g., resolution filtering, normalization) and alignment to model inputs/outputs.
• Cryo‑EM expertise: Operationalize end‑to‑end flows from raw image stacks/particles to 3D maps and model‑ready tensors; interoperate with community formats (e.g., EMDB/EMPIAR, mmCIF) and link to sequences/annotations.
• Signal & information content: Design dataset diagnostics (e.g., mutual‑information‑like measures, effective sample size, SNR proxies) to quantify what data teach the model; build active‑learning loops that maximize learning per euro of data collection time.
• Model‑aware data services: Implement scalable, versioned data services and feature stores that feed training/evaluation; design loaders/augmentations optimized for throughput and correctness (GPU‑aware).
• Training‑at‑scale engineering: Own distributed data pipelines and orchestration for large runs on Azure; profile and tune I/O, storage tiers, data locality, and caching; monitor cost, utilization, and failure modes.
• Quality, governance, and reproducibility: Codify schemas/ontologies, metadata contracts, unit/integration tests, and lineage; automate validation and data drift detection; maintain documentation and examples.
• Partner across disciplines: Work closely with ML researchers, structural biologists, and drug designers; translate experimental constraints into robust computational workflows; communicate clearly and proactively.

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.

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.