דרושים חוקר ב-Microsoft ב-Taiwan, Taoyuan City

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.

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.

• 2-3 years of experience in security research, detection engineering, threat lifecycle, cloud security in large-scale in complex cloud environments, as well as disciplines related to managed services for SIEM platforms.
• Proven ability to execute advanced research on emerging cloud-based threats affecting both Microsoft and third-party security products across heterogeneous cloud environments.
• Demonstrated experience in developing and refining detection and response strategies leveraging major SIEM platforms, with a strong emphasis on Microsoft Sentinel, to provide comprehensive threat coverage and response capabilities.
• Extensive hands-on experience with cloud platforms—including, but not limited to, Azure—as well as an understanding of multi-cloud security challenges and solutions.
• Strong practical experience identifying, analyzing, and mitigating real-world cyber threats in complex cloud environments.
• Proven ability to work independently and deliver complete solutions.
• Demonstrated capability to effectively articulate technical insights and influence multidisciplinary teams

Responsibilities include:

• Execute advanced research on emerging cloud-based threats impacting Microsoft and third-party security products across heterogeneous cloud environments.

• Develop and refine detection and response strategies leveraging major SIEM platforms, with a strong emphasis on Microsoft Sentinel, to provide comprehensive threat coverage and response capabilities.

• Collaborate with internal and external security teams to implement scalable, innovative solutions for multi-cloud threat intelligence, detection, mitigation and response.

• Translate complex raw security data into actionable intelligence that enhances the effectiveness of cloud security operations for a global customer base.

• Mentor, guide, and drive best practices among researchers and detection engineers on advanced threat hunting and incident response across diverse SIEM ecosystems.

• Contribute to industry knowledge and Microsoft’s security posture by publishing research, developing threat models, and proactively identifying threats and attack trends in the cloud.

and looking for a Researcher in Foundation Model innovations (LLM, Vision, Multi-modality, etc.), Deep Learning and Reinforcement Learning Foundation, AI for Healthcare, Embodied AI and Robotics, Societal AI, Industrial AI application, etc.

• Doctorate in relevant field AND 1+ year(s) related research experience OR equivalent experience.
• Experience developing AI solutions for real-world industry applications.
• Experience publishing academic papers as a lead author or essential contributor.
• Deep knowledge about latest large foundation models and their applications
• Strong communication skills to engage with various collaborators both externally and internally
• Keen interest in general AI research, including but not limited to large foundation models, generative AI, multimodal learning, resource-efficient learning and Industry AI.

• Conduct cutting-edge research in large foundation models and generative AI, focusing on applying large foundation models and generative AI in real-world industrial scenarios.
• Collaborate with cross-functional teams to develop AI solutions to address real-world problems in various industry sectors.
• Develop and maintain research prototypes and software tools, ensuring that they are well-documented and adhere to best practices in software development.
• Publish research findings in top-tier conferences and journals and present your work at industry events.
• Collaborate with other AI researchers and engineers, sharing knowledge and expertise to foster a culture of innovation and continuous learning within the team.

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 research experience in Computer Science, Machine Learning, Physics, or a related field.
• Demonstrated leadership in ML architecture and algorithm design.
• Strong expertise in deep learning (model design, large-scale training, evaluation and reproducibility), statistics and linear algebra.
• Proficiency in Python and modern ML/scientific frameworks (e.g., PyTorch, JAX, TensorFlow, NumPy, SciPy, Pandas).
• Peer-reviewed publications in leading venues (e.g., NeurIPS, ICML, ICLR or leading journals).
• Excellent technical communication for collaborating in an interdisciplinary team.
• Curiosity and drive to apply deep learning to biological problems.
• Comfort with real‑world, noisy/heterogeneous data.

Preferred:

• ML Engineering skills (e.g., model optimization and deployment, code design, CUDA).
• Experience with biomolecular modeling or bioinformatics (e.g., folding systems, structural analysis/visualization, MD simulation, structure/genome databases).
• Ability to work with and interpret real‑world biological data (e.g., cryo‑EM, protein binding affinities, structural/biophysical measurements).

• Invent novel deep learning techniques for models of biomolecular structure, dynamics, and function.
• Design, implement, and iterate on model architectures and training algorithms (e.g., diffusion/sequence–structure models, representation learning); run rigorous ablations and baselines.
• Define success where standards don’t exist: proposing sound benchmarks and uncertainty‑aware metrics that reflect real‑world utility.
• Build high‑quality research code (Python/PyTorch) with reproducible work-flows and robust data pipelines.
• Partner across disciplines—communicate clearly with ML researchers and experimental/computational biologists; present results and influence direction.
• Work autonomously and as a team player, reporting insights, risks, and next steps with crisp written/visual summaries.
• Thrive with imperfect, heterogeneous data, using principled curation, augmentation, and probabilistic evaluation.
• Aim for impact: try ideas quickly and fail-fast when they don't work. Rapidly convert working ideas to artifacts others can use (code, models, datasets, papers, patents).

Principal Researcherinquery execution, data storage,esource management forserverless cloud platdata discoveryand data preparation tecandidates withinnovel hardware,Artificial Intelligence, and Machine Learningfor data management challenges areof special interest to us.

such as SQL Server,Azure SQL Database,Power BI,We have hadinfluential publications at top conferences such as ACM SIGMODand IEEE ICDEbest paper awards and test-of-time awards.More details are available at

Required Qualifications

• in Computer Science, Computer Engineering,or relevant fieldAND 3+ years related research experience
  • OR equivalent experience
• A track recordof research accomplishments, e.g.,majorconference publications(ACMSIGMOD, VLDB)

Preferred Qualifications

• Doctorate in relevant field AND 5+ years related research experience) OR equivalent experience
• Experience publishing academic papers as a lead author or essential contributor
• Appetite for undertaking ambitious research challenges
• Familiarity with traditional SQL or big data systemscloud platforms
• Expertiseinkey components of data systems, e.g.,distributedquery execution, resource management for cloud platforms, low-code data transformations
• In-depthknowledgeof modern hardware
• Experience with applying AI and Machine Learning techniques for challenges in data system

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

• Build anddeploynovel systems

• Contribute significant innovations to

• Publish papers on significant researchresults

• Embody our and

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 research experience in Chemistry, Biophysics, Physics, Computer Science, Bioinformatics or a related field.
• Track record of impactful research on biomolecular structure/dynamics or ML for biology (e.g., strong publications, open‑source, or shipped analyses).
• Excellent technical communication for interdisciplinary work.
• Comfortable with real‑world data that lack structure/cleanliness/completeness.
• Fluency building scalable Python pipelines; experience running large studies or analyses on cloud/HPC.

Preferred:

• Industry experience in pharma/biotech or industrial research.
• Experience guiding large‑scale experimental data generation (e.g., cryo‑EM campaigns, screening, binding/functional assays), including QC/QA, metadata, and data governance.
• Familiarity with model‑guided experimental design (active learning) and uncertainty quantification.
• Experience with distributed ML training/inference and cost/performance trade‑offs on HPC or cloud systems.

• Scientific leadership without silos: lead an effort to apply BioEmu at scale to generate biological insights and hypothesis for drug discovery. Be the principal contact point for world-leading collaborators and drug hunters. Contribute to and closely align with project leadership to strive for maximum impact. Prioritize team success over personal agenda.
• Model biomolecular structure and dynamics at scale: Use BioEmu, structure/sequence databases, and complementary tools to analyze proteins and complexes.
• Collaboration with drug designers & external partners: Lead day‑to‑day technical collaboration with pharma/biotech partners and CROs; define joint objectives, success metrics, and Go/No‑Go criteria; ensure traceability and reproducibility of decisions.
• Support and oversee data generation at scale: Design prospective studies with wet‑lab teams (e.g., binding/kinetics assays, structural campaigns such as cryo‑EM or crystallography); plan sample selection via active learning/uncertainty; manage data quality, metadata standards, and data rights.
• Generate biological hypotheses and insights: Integrate biomolecular structure and dynamics data to generate experimentally-testable predictions and biological insights.
• Systems & tooling at scale: Guide robust pipelines for large‑scale inference/analysis on Azure; ensure cost‑aware compute/storage use; codify best practices in code and documentation.
• Stakeholder communication: Author clear technical narratives for collaborators and internal leadership; present results credibly to mixed audiences; preempt risk with measurable mitigation plans.
• Mentor & uplift: Manage and coach applied researchers and engineers.