New 
Senior AI Applied Scientist
 Microsoft | |
 United States, Washington, Redmond | |
 Aug 23, 2025 | |
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OverviewWe are looking for a Senior AI Applied Scientist to join our team.As a Senior AI Applied Scientist, 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. 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. This role will combine AI knowledge with applied science expertise, and demonstrate a growth mindset and customer empathy. 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.
ResponsibilitiesBringing 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, 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 Demonstrate deep expertise in AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models) to translate cutting-edge research into practical, real-world solutions that drive product innovation and business impact. Share insights on industry trends and applied technologies with engineering and product teams. Formulate strategic plans that integrate state-of-the-art research to meet business goals. Documentation Maintain clear documentation of experiments, results, and methodologies. Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing 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, identifying optimal features and addressing 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. | |