Principal AI Applied Scientist

Microsoft Corporation • Redmond, WA

35d

About The Position

As a Principal AI Applied Scientist for The Customer Service Applications Team, 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 of cutting-edge AI technologies into Microsoft products and services, ensuring they are inclusive, ethical, and impactful. You will collaborate across product, research and engineering teams to bring innovative solutions to life, applying your expertise in machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experiences. AI Mission and Impact We are in an era of unprecedented innovation and openness. As Microsoft continues to lead in AI, we are seeking individuals to help tackle some of the most exciting and meaningful challenges in the field. Our vision is to build a truly open architecture platform that enables users to summon tailored AI agents to drive real-world outcomes. We are looking for a Principal AI Applied Scientist to join our team This role will combine AI knowledge with applied science expertise and demonstrate a growth mindset and customer empathy. Join us in shaping the future of AI agents. 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.

Requirements

• Bachelor'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 Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• 8+ years of experience leading large-scale AI systems and cross-org initiatives that shipped.
• 8+ years of experience in software engineering foundations and hands-on depth in Python plus deep-learning frameworks (PyTorch/ TensorFlow) and modern MLOps/tooling.
• 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 Cloud Background Check upon hire/transfer and every two years thereafter.

Nice To Haves

• PhD in AI/ML or related field with top-venue publications and/or patents.
• Experience architecting and deploying LLMs/multimodal models and multi-agent systems in production at scale.
• Familiarity with Responsible AI frameworks and bias-mitigation techniques.
• Demonstrated ability to shape product strategy and drive organizational change.
• Experience with Microsoft's LLMOps stack: Azure AI Foundry, Azure Machine Learning, Semantic Kernel, Azure OpenAI Service, and Azure AI Search for vector/RAG

Responsibilities

• Bringing the State of the Art to Products
• Build collaborative relationships with product and business groups to deliver AI-driven impact
• Research and implement state-of-the-art using foundation models, prompt engineering, RAG, graphs, multi-agent architectures, as well as classical machine learning techniques.
• Fine-tune foundation models using domain-specific datasets. - Evaluate model behavior on relevance, bias, hallucination, and response quality via offline evaluations, shadow experiments, online experiments, and ROI analysis.
• Build rapid AI solution prototypes, contribute to production deployment of these solutions, debug production code, and support MLOps/AIOps.
• Contribute to papers, patents, and conference presentations. - Translate research into production-ready solutions and measure their impact through A/B testing and telemetry that address customer needs.
• Ability to use data to identify gaps in AI quality, uncover insights, and implement PoCs to show proof of concepts.
• Leveraging Research in real-world problems
• Drive original research and thought leadership (whitepapers, internal notes, patents); convert insights into shipped capabilities.
• Research Translation: Continuously review emerging work; identify high-potential methods and adapt them to Microsoft problem spaces.
• Production Integration: Turn research prototypes into production-quality code optimized for scale, latency, and maintainability.
• ML Design & Architecture: Own end-to-end pipeline from data prep, training, evaluation, deployment, and feedback loops.
• Evaluation & Instrumentation: Build robust offline/online evals, experimentation frameworks, and telemetry for model/system performance.
• Learning Loop Creation: Operationalize continuous learning from user feedback and system signals; close the loop from experimentation to deployment.
• Experimentation & E2E Validation: Design controlled experiments, analyze results, and drive product/model decisions with data.
• Cross-Functional Collaboration & Influence
• Broker collaborations across Microsoft Research, product engineering, and external partners.
• Ethics, Privacy and Security
• Apply a deep understanding of fairness and bias in AI by proactively identifying and mitigating ethical and security risks-including XPIA (Cross-Prompt Injection Attack) unfairness, bias, and privacy concerns-to ensure equitable and responsible outcomes.
• Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.
• Contribute to internal ethics and privacy policies and ensure responsible AI practice throughout AI development cycle from data collection to model development, deployment, and monitoring.
• Specialty Responsibilities
• Design, develop, and integrate generative AI solutions using foundation models and more.
• Deep understanding of small and large language models architecture, Deep learning, fine tuning techniques, multi-agent architectures, classical ML, and optimization techniques to adapt out-of-the-box solutions to particular business problems
• Prepare and analyze data for machine learning, identify optimal features, and address data gaps.
• Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks and state-of-the-art models, open-source libraries, statistical tools, and rigorous metrics
• Address scalability and performance issues using large-scale computing frameworks.
• Monitor model behavior, , guide product monitoring and alerting, and adapt to changes in data streams.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Publishing Industries
• Number of Employees: 5,001-10,000 employees