New 
Principal AI Applied Scientist
 Microsoft | |
 United States, Washington, Redmond | |
 Nov 02, 2025 | |
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OverviewAs a Principal AI Applied ScientistforThe 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 ofcutting-edgeAI technologies into Microsoft products and services, ensuring they are inclusive, ethical, and impactful. You will collaborate acrossproduct,researchand engineering teams to bring innovative solutions to life, applying yourexpertisein machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experiences. AIMission and Impact We are in an era of unprecedented innovation and openness. As Microsoft continues tolead inAI, we are seeking individuals to help tackle some of the most exciting and meaningful challenges in the field. Our vision is to builda 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 Scientistto join our team This rolewill combineAI knowledge withapplied scienceexpertise anddemonstrate a growth mindsetandcustomer 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. Asemployees,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.
ResponsibilitiesBringing the State of the Art to Products Build collaborative relationships with product and business groups to deliver AI-driven impact Research and implementstate-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, and supportMLOps/AIOps. Contribute to papers, patents, and conferencepresentations. -Translate research into production-ready solutionsand measure their impact through A/B testing and telemetrythat addresscustomer needs. Ability to use data toidentifygaps in AI quality, uncoverinsights,and implementPoCsto show proof of concepts. Leveraging Researchin real-world problems Drive original research and thought leadership (whitepapers, internal notes, patents); convert insights into shipped capabilities. Research Translation: Continuously review emerging work;identifyhigh-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 pipelinefrom 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-FunctionalCollaboration &Influence Broker collaborations across Microsoft Research, product engineering, and external partners. Ethics,Privacyand Security Apply a deep understanding of fairness and bias in AI by proactivelyidentifyingand mitigating ethical and security risks-includingXPIA(Cross-Prompt Injection Attack)unfairness, bias, and privacy concerns-to ensureequitableand responsible outcomes. 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,identifyoptimalfeatures,andaddressdata gaps. Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks andstate-of-the-artmodels, open-source libraries, statistical tools, and rigorous metrics Address scalability and performance issues using large-scale computing frameworks. Monitor model behavior,,guide product monitoring andalerting,andadapt to changes in data streams. | |