Principal Researcher – Artificial Intelligence Microsoft Research 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 candidate will leverage external and internal data to identify talent pools, assess market feasibility, and uncover competitive trends. You will design and implement sourcing strategies that go beyond traditional methods—using intelligence to anticipate and track talent movement, research competitors and industry trends, and optimize our location strategies.

Required/minimum qualifications

• Bachelor's Degree AND 6+ years talent acquisition experience (e.g., recruiting, sourcing), HR experience, or industry experience related to role
  • OR equivalent experience.
• 4+ years of talent and market intelligence experience

Additional or preferred qualifications

• Bachelor's Degree AND 12+ years talent acquisition experience (e.g., recruiting, sourcing), HR experience, or industry experience related to role
  • OR Master's Degree AND 8+ years talent acquisition experience (e.g., recruiting, sourcing), HR experience, or industry experience related to role
  • OR equivalent experience.
• 4+ years of market research to map and track talent, key competitors, and industry trends.
• 4+ years of documenting and delivering actionable insights of market research to formulate a differential sourcing strategy.

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.

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.

As an(AM) focused on Microsoft Advertising, you will enable Microsoft to better serve clients by applying your obsession to what clients care about and need, ensuring our story is valued and understood, and building and maintaining solid trusted-advisor relationships with day-to-day practitioner contacts at advertiser/agency. You will drive both revenue and client satisfaction across MSA’s performance solutions and as a core member of the account team, this opportunity will allow you to focus on driving data-driven optimizations aligned with client needs, executing campaigns and driving solid coordination or internal resources for excellence in execution of core tasks, account transitions, and client account health.

Required Qualifications:

• Master of Business Administration (MBA) Degree in Sales, Marketing, or related field and 2+ years relevant work experience (e.g., sales, advertising, or marketing with a focus on digital advertising) OR Bachelor's Degree in Sales, Marketing, or related field and 4+ years relevant work experience (e.g., sales, advertising, or marketing with a focus on digital advertising) OR 6+ years relevant work experience (e.g., sales, advertising, or marketing with a focus on digital advertising) or equivalent experience.
• 1+ year of experience managing hands-on search advertising campaigns and other performance-based digital marketing campaigns.

Preferred Qualifications:

• Master of Business Administration (MBA) Degree in Sales, Marketing, or related field and 4+ years relevant work experience (e.g., sales, advertising, or marketing with a focus on digital advertising) OR Bachelor's Degree in Sales, Marketing, or related field AND 5+ years relevant work experience (e.g., sales, advertising, or marketing with a focus on digital advertising) OR 8+ years relevant work experience (e.g., sales, advertising, or marketing with a focus on digital advertising) OR equivalent experience.
  
• 2+ years of experience managing hands-on search advertising campaigns and other performance-based digital marketing campaigns.
  
• 2+ years of experience in online media or Search Engine Marketing Sales and Service 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 19, 2025.

• Proactively solicits feedback on additional advertising needs, products, and features and ensures the feedback reaches the right stakeholders internally through relevant channels to gain a further understanding of market needs and client expectations and to drive solutions on behalf of the clients.
• Builds and maintains a solid network of internal and external partner resources to drive opportunities and influence impact for clients. Collaborates with partners to develop and share client plans that support Microsoft business and client needs.
• Establishes oneself as the primary point of contact to address day-to-day inquiries. Engages clients in a proactive manner to address day-to-day questions related to account monitoring (e.g., what to prioritize, managing escalations, billing, compliance requirements, flags, or reactions), managing client needs, and addressing issues.
• Builds and maintains relationships with day-to-day client contacts as a trusted advisor throughout multiple levels.
• Seeks client feedback (both formal and informal) to identify and understand the drivers of satisfaction and understand the drivers of dissatisfaction, determine the root cause of problems, and remove blockers. Establishes recovery action plans to improve the client's overall experience and avoid future instances of dissatisfaction.
• Articulates Microsoft's strategy and other products and services, and educates clients on the Microsoft Advertising platform. Shares knowledge of the search landscape with clients to create a mutually-beneficial business value proposition and executes relevant solutions.
• Adopts Microsoft sales strategies (e.g., Challenger Sale) and handles main objections from clients. Works to overcome obstacles, sell tailored solutions, and secure buy-in and execution.
• Leverages Customer Relationship Management (CRM) tools (e.g., account management, opportunities, tracking, and reporting), other tools (e.g., customized performance dashboards, data, insights), and ad hoc opportunities to identify ways to keep up with account health, increase performance, satisfaction, and upsell opportunities to increase revenue.
• Collaborates with internal partners and peers to develop and implement strategies to identify and maximize upselling opportunities through value-proposition presentations and/or to help Account Executives accelerate the closing of deals.
• Proactively researches information about clients' advertising needs (e.g., advertising key performance indicators [KPIs]) to learn more about their competition and market trends. Identifies opportunities to drive optimizations and new solutions based on clients' advertising strategies.
• Presents tailored solutions based on clients' insights.
• Maintains knowledge of emerging industry trends beyond a singular product as well as of technology and key performance indicators (KPIs).
• Leverages competitive trends and shares knowledge with others as a subject matter expert.
• Embody and promote Microsoft’s and .

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.