Overview
Overview
The
Customer Experience and Engineering (CXP)
organization drives Microsoft’s internal AI transformation and sets the benchmark for enterprise-ready solutions. Our mission is to run and grow Microsoft’s business by delivering secure, compliant, and performant platforms while leading operational transformation through Copilots and agents. We partner with Business Leaders to scale innovation, collaborate with BIC teams to shape enterprise-grade products, and serve as a reference for AI adoption across industries. Guided by principles of agility, data-driven decision-making, and deep integration with Microsoft technologies, we prioritize productization, co-development, and extensibility to maximize impact. By leveraging insights from our own transformation and customer journeys, we ensure solutions meet evolving needs and deliver measurable business value.This is an exciting time to join our group CXP and work on something highly strategic to Microsoft. The goal of CXP is to build the next generation of our applications running on Microsoft first party products including Azure ML, Azure AI Foundry, Dynamics 365, MCS, and several other Microsoft cloud services to deliver high value, complete, and Copilot-enabled application scenarios across all devices and form factors. We innovate quickly and collaborate closely with our partners and customers in an agile, high-energy environment. Leveraging the scalability and value from Azure & Power Platform, we ensure our solutions are robust and efficient. If the opportunity to collaborate with a diverse engineering team, on enabling end-to-end business scenarios using cutting-edge technologies and to solve challenging problems for large scale 24x7 business SaaS applications excite you, please come and talk to us! Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. We are seeking a
Senior Applied AI Scientist
to join the Customer Experience (CXP) Data Science team, driving Microsoft’s AI transformation across Sales, Support, and Marketing. In this role, you will design and deploy advanced AI solutions, including large language models (LLMs), retrieval-augmented generation (RAG), and agentic architecture - while partnering closely with engineering and product teams. You will lead experimentation, build scalable ML pipelines, and develop evaluation frameworks to ensure reliability, fairness, and measurable business impact. This is an opportunity to work on cutting-edge AI technologies that power Copilot experiences and deliver transformative outcomes for millions of customers worldwide.
Responsibilities
Key Responsibilities
- ML & AI Development
- Lead the research, design, and development of advanced ML and AI models, ensuring high performance, accuracy, and robustness.
- Develop novel algorithms and architectures, optimizing real-world deployment constraints such as latency, efficiency, and scalability.
- Leverage cutting-edge advancements in deep learning, generative AI, reinforcement learning, and large-scale ML systems to push the boundaries of AI innovation.
- Scalable Model Deployment & Optimization
- Build and deploy ML/AI models at scale, ensuring seamless integration into production systems with minimal latency and maximum efficiency.
- Optimize models for performance and efficiency using techniques such as fine tuning, quantization, pruning, distillation, and hardware acceleration (e.g., GPUs, TPUs, FPGAs).
- Drive best practices for A/B testing, model evaluation, and hyperparameter tuning to continuously improve model performance.
- ML Architecture & Automation
- Design and implement scalable ML architectures that support real-time inference, batch processing, and hybrid AI workflows.
- Develop robust pipelines for data preprocessing, feature engineering, model training, and deployment, ensuring high-quality input data and reproducibility.
- Ensure efficient retraining and model versioning, enabling rapid experimentation and continuous learning in production environments.
- AI Model Governance, Security & Compliance
- Ensure all ML models adhere to security, privacy, and ethical AI standards, including fairness, explainability, and regulatory compliance.
- Implement techniques for bias detection, adversarial robustness, and secure AI deployment to mitigate risks in real-world applications.
- Establish best practices for model monitoring, drift detection, and performance tracking, ensuring AI systems remain reliable and effective.
- Cross-Functional Collaboration & AI Strategy
- Work closely with data science, engineering, and product teams to align AI initiatives with business objectives and technical feasibility.
- Influence the broader AI roadmap, advocating new methodologies, frameworks, and tools to enhance the impact of ML models.
- Communicate complex ML concepts and results to senior leadership, product teams, and stakeholders, ensuring alignment on AI strategies and outcomes.
- Research, Innovation & AI Thought Leadership
- Stay at the forefront of AI and ML research, actively exploring new algorithms, architectures, and applications in deep learning, NLP, CV, and more.
- Lead proof-of-concept (PoC) projects, testing and validating emerging AI technologies for potential production adoption.
- Contribute to AI research communities, publish papers, attend conferences, and engage in collaborations with academia and industry partners.
- Mentorship & AI Talent Development
- Mentor and guide junior and mid-level ML scientists, fostering a culture of innovation, experimentation, and continuous learning.
- Lead technical deep dives, AI model reviews, and algorithmic discussions, helping the team stay ahead of industry trends.
- Identify skill gaps and drive AI education initiatives, ensuring the team is proficient in state-of-the-art ML methodologies.
Qualifications
Minimum Qualifications:
- B.Sc in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, or a related field.
- Industry Experience: 5+ years of hands-on experience in designing, developing, and deploying machine learning models at scale in production environments.
- ML & AI Expertise: Strong theoretical and practical knowledge of supervised and unsupervised learning, deep learning, generative AI, reinforcement learning, probabilistic modeling, and large-scale ML systems.
- Programming & Development Skills: Proficiency in Python with deep expertise in ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, Hugging Face, or similar.
- Model Deployment Experience: Experience in deploying and optimizing ML models in cloud-based environments (Azure, AWS, GCP).
- Data Handling & Feature Engineering: Expertise in working with large-scale datasets, time-series data, structured/unstructured data, and applying advanced feature engineering techniques.
- Mathematical & Statistical Proficiency: Strong foundation in linear algebra, probability, optimization, Bayesian inference, and numerical methods.
- Cross-Functional Collaboration: Ability to work closely with software engineers, product managers, and business stakeholders to translate business needs into AI solutions.
- Communication Skills: Ability to clearly articulate complex ML concepts, write technical reports, and present findings to both technical and non-technical audiences.
Preferred Qualifications
- Ph.D. in ML/AI or Related Field: Strong research background with contributions to top-tier ML/AI conferences (NeurIPS, ICML, CVPR, ACL, etc.).
- Experience with Large-Scale AI Systems: Experience working with LLMs, foundation models, multimodal learning, transformers, and generative AI for real-world applications.
- High-Performance ML Optimization: Expertise in model compression, quantization, distillation, low-rank adaptation (LoRA), and hardware acceleration (GPUs, TPUs, FPGAs).
- Cloud & Distributed Computing: Experience with Kubernetes, Spark, Ray, Dask, or other distributed computing frameworks for scalable AI training and inference.
- Responsible AI & Compliance: Familiarity with fairness, interpretability, privacy-preserving AI (e.g., differential privacy, federated learning), and AI governance frameworks.
- End-to-End AI Product Development: Experience integrating ML models into real-time applications, APIs, or enterprise software solutions.
- Patents & Publications: Demonstrated contributions to AI innovation through patents, research papers, or open-source projects.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about
requesting accommodations.
