Martech & Ai Governance Director jobs at Microsoft in Taiwan, Taoyuan City

Minimum Experience

• 5+ years technical pre-sales or technical consulting experience AND Bachelor's Degree in Computer Science, Information Technology Additional

  • or preferred qualifications 4+ years experience with cloud and hybrid

  • or on premises infrastructures, architecture designs, migrations, industry standards, and/or technology management Certification in relevant technologies or disciplines (Azure Architect and Development exams, Cloud Platform Technologies, Architecture).

• Drive technical sales conversations with business and technical decision makers, using demos and proof-of-concepts to shape solution design and accelerate production deployments.
• Lead hands-on engagements such as hackathons, code-with sessions, and architecture workshops to fast-track adoption of Microsoft developer tools and cloud platforms.
• Build trusted relationships with developers and platform leaders, co-designing secure, scalable, and innovative architectures.
• Resolve technical blockers and objections, working closely with engineering teams to provide feedback and influence product improvements.
• Develop and maintain expertise in AI Foundry and AI application architecture, including Agentic AI frameworks, Semantic Kernel, Foundry SDK, and Responsible AI.
• Grow skills in cloud-native development, including APIs, containerization, microservices, event-driven systems, and programming in Python, Java, or .NET.
• Expand knowledge in AI management and security, including GenAI Ops, Sentinel, orchestration, and monitoring.
• Represent Microsoft as a thought leader in developer communities and customer forums.

Required/Minimum Qualifications:

• Bachelor’s degree in computer science, Information Technology, Engineering, or a related field AND 6+ years of experience in cloud/infrastructure technologies, software development/support, analytics/BI system development, or artificial intelligence (AI).

• Deep understanding of Cloud Infrastructure, Cloud Native solutions, containerization, and Dev sec ops.

• Familiarity with at least one development language (e.g., Python, Java, Go, C#, JavaScript, Scala).

• Problem Solving. Ability to solve customer problems through cloud technologies required

• 6+ years of experience in customer-facing IT project delivery.

Additional or Preferred Qualifications:

• Master’s degree in computer science, Information Technology, Engineering, or a related field AND 6+ years of experience in cloud/infrastructure technologies, software development/support, analytics/BI system development, or artificial intelligence (AI).

• Proficiency in multiple development languages (e.g., Python, Java, Go, C#, JavaScript, Scala).

• 8+ years of experience in customer-facing IT project delivery.

• 8+ years of experience leading technical projects.

• Technical Certification in Cloud (e.g., Azure, Amazon Web Services, Google Cloud, Alibaba Cloud, Tencent Cloud, security certifications).

Essential Skills:

• OpenAI/AI, Llama, Deepseek, Copilot including Github Copilot and Studio, and Cognitive services
• Microservices and containerization with Azure Kubernetes Services
• Advanced Analytics (Azure Synapse, Databricks, visualization tools such as PowerBI, Tableau, QuickBI)
• NoSQL Databases including OSS (Cassandra, Mongo, HBase, etc.), Cosmos DB

• Understand customers’ overall data estate, IT and business priorities and success measures to design implementation AI based architectures and solutions.

• Consumption (Cloud & Support) growth - Develop opportunities to drive Customer Success business results & help Customers get value from their Microsoft investments.

• Apply technical knowledge to architect solutions that meet business and IT needs, be advisor for customer’s Azure AI Platform strategy, applications modernization through microservices on Azure Kubernetes services, AA/AI roadmaps, and ensure long term technical viability of new deployments, infusing key analytics and AI technologies where appropriate (e.g. Azure ML, Azure AI Services including AI Search, Azure OpenAI, Copilot including Github Copilot and Studio, Cognitive Services, Spark, Data Lake, Azure Synapse, etc.)

• Ensure that solution exhibits high levels of performance, security, scalability, maintainability, appropriate reusability and reliability upon deployment.

• Be a Voice of Customer to share insights and best practices, connect with Engineering team to remove key blockers. Deliver according to MS best practices & using repeatable Intellectual Property (IP) and Value Based Delivery (VBD).

• Assess the Customers' knowledge of Azure platform and overall cloud readiness to support customers through a structured learning plan and ensure its delivery through partners.

• Collaborate with other Solution Engineers and other Cloud Solution Architects in developing complex end-to-end Enterprise solutions on Microsoft Azure platform.

• Maintain technical skills and knowledge, keeping up to date with market trends and competitive insights; collaborate and share with the technical community while educate customers on Azure platform.

• Be an Azure Platform evangelist with customers, partners and external communities.

Required Qualifications

• Master's Degree in Computer Science, Information Technology, or related field AND 4+ years technical pre-sales or technical consulting experience OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 6+ years technical pre-sales or technical consulting experience OR 7+ years technical pre-sales or technical consulting experience OR equivalent experience.

Preferred Qualifications

• 10+ years technical pre-sales, technical consulting, or technology delivery, or related experience OR equivalent experience.
• 8+ years experience with cloud and hybrid, or on premises infrastructures, architecture designs, migrations, industry standards, and/or technology management.
• Certification in relevant technologies or disciplines (e.g., Office 365, Power BI, Azure Architect and Development exams, Cloud Platform Technologies, Information Security, Architecture).

Solution Design and Proof

Adapts and extends architecture patterns to accommodate complex customer requirements and drive integration solutions for industry flavor. Delivers assets that can be leveraged by others in the business.

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.

Required/minimum qualifications

Bachelor's Degree in Computer Science, Information Technology, Business Administration, Information Security, or related field AND 10+ years experience in technology-related sales or account management OR equivalent experience.

Additional or preferred qualifications

Master's Degree in Business Administration (i.e., MBA), Information Technology, Information Security, or related field AND 8+ years experience in technology-related sales or account management OR Bachelor's Degree in Computer Science, Information Technology, Business Administration, Information Security, or related field AND 12+ years experience in technology-related sales or account management OR equivalent experience.

6+ years solution or services sales experience.

Sales & Pipeline Management

Sales Strategy

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.