Research Science Internship Opportunities jobs at Microsoft in Taiwan, Taoyuan City

As a Software Engineering PhD Intern, you will work with teammates to solve problems and build innovative software solutions. You will apply your passion for customers and product quality as you provide technical guidance to Technical Program Managers and Product Managers. You will learn and adopt relevant new technologies, tools, methods, and processes to leverage in your solutions. This opportunity will enable you to advance your career by designing, developing, and testing next-generation software that will empower every person and organization on the planet to achieve more.

Required Qualifications

• Pursuing a PhD in Computer Science, Engineering, or related field during the academic term immediately before the internship.

• Must have at least 1 semester/term remaining following the completion of the internship.

• At least one year of programming experience in an object-oriented language.

Preferred Qualifications

• Ability to demonstrate an understanding of computer science fundamentals, including data structures and algorithms.
• Academic/research experience with distributed systems, network architecture, operating systems, and/or related topics.

The base pay range for this internship is USD $5610.00 - $11010.00 per month. 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 $7270.00 - $12030.00 per month.

• Applies engineering principles to solve complex problems through sound and creative engineering.

• Works with appropriate stakeholders to determine user requirements for a feature.

• Quickly learns new engineering methods and incorporates them into work processes.

• Seeks feedback and applies internal or industry best practices to improve technical solutions.

• Demonstrates skill in time management and completing software projects in a cooperative team environment.

• Reviews current developments and proactively seeks new knowledge that will improve the availability, reliability, efficiency, observability, and performance of products while also driving consistency in monitoring and operations at scale.

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.

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.

Required Qualifications

• Currently pursuing aPhD/ Master’s degree in Computer Science, Engineering, Data Science, Mathematics, or related field.
• Must have at least three additional semesters of school remaining (graduation date 2027 or later)
• Solid background in machine learning, deep learning, language models and optimization.

Preferred Qualifications

• Currently pursuing a PhD inComputer Science, Engineering, Data Science, Mathematics, or related field.
• Proficiency in Python and experience with PyTorch, TensorFlow and ONNX.
• Research or hands-on experience in efficient model architecture and model compression techniques (quantization, pruning, distillation, etc.).
• Research or hands-on experience with language models (e.g., BERT, LLaMA, GPT).
• Publications in top-tier relevant ML venues (e.g., NeurIPS, ICML, ACL, or ICLR).
• Previous industry experience as a Data Scientist / Intern.
• Solid background in machine learning, deep learning, language models and optimization.
• Strong analytical thinking and independent research capabilities.
• Excellent communication, collaboration, and writing skills.

• Design, implement, and evaluate methods to enable efficient, secure and optimized deployment of AI models on edge or resource-constrained platforms.
• Drive innovation and practical research, iteratively improving solutions based on empirical results, working with large-scale textual datasets.
• Document research findings and contribute to publications, open-source projects, or patent filings as appropriate.
• Formulate approaches to solve problems using well defined algorithms and data sources
• Incorporate an understanding of product functionality and customer perspective to provide context for those problems.
• Engage with peer stakeholders to produce clear, compelling, actionable insights that influence product and service improvements that will impact millions of customers.

Required/Minimum 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.
• 5+ years people-management experience.
• Experiencehandling large data sets to reveal patterns, trends, and associations, from behavioral and interaction data to uncover important factors that can influence outcomes.
• Experience with one or more AI/ML algorithms and frameworks and their application to solve various business problems. Examples include causal inference, resampling techniques, mixed effects models, clustering, classification, significance tests, time-series modeling/forecasting, etc.

Additional or Preferred Qualifications

• Doctorate 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 Master'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 Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 15+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• 7+ years people-management experience.

• Experience with Large Language Models (LLMs), prompt engineering, and Azure AI Application Programming Interfaces (APIs)
• Experience with Machine Learning Operations (MLOps) practices: containerization, infrastructure-as-code, monitoring
• Experience with multi-agent frameworks like AutoGen, Semantic Kernel, and Langchain
• Experience in debugging, security, and data protection principles

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 25, 2025.

• Demonstrate a commitment to Microsoft’s data security and privacy standards as the top priority in all analytics, reports, and solutions we develop.
• Lead large, complex projects to deliver accurate, accessible, secure and repeatable data products aligned to business and organizational needs and priorities that enhance our ability to inform marketing efforts, increase effectiveness of our approaches and deliver measurable outcomes.
• Demonstrate leadership in business and data landscape, visualizations, experimentation, and analysis to effectively communicate and support data driven decision making.
• Foster a team that embraces and lives Microsoft’s Culture and Values.
• Design and implement repeatable and scalable statistical and Machine Learning (ML) based frameworks to identify changes in behavior, business performance and other outcomes to help us make predictions and recommendations, set forecasts, identify anomalous activities, and understand outcome trajectory.
• Partner with Data Services on technical requirements to ensure adherence to security and compliance guidelines and enable data availability, data quality and integrity for data science, analytics and wider business self-service usage.
• Establish processes for the development of experimental methodologies, statistics, optimization, and probability theory for general purpose software and statistical packages. Leverages results from modeling and analyses to influence business and/or product strategy and leadership across teams.
• Deliver success through empowerment and accountability by modeling, coaching, and caring. Model: Live our culture. Embody our values. Practice our leadership principles. Coach: Define team objectives and outcomes. Enable success across boundaries. Help the team adapt and learn. Care: Attract and retain great people. Know each individual’s capabilities and aspirations. Invest in the growth of others.
• Embody our and