About this role

Overview The Purview AI Classification team builds advanced AI capabilities to identify and classify sensitive information at enterprise scale, with a strong focus on Named Entity Recognition (NER) and rigorous AI evaluation. We develop and improve ML/NLP models for complex entity recognition scenarios and build evaluation methodologies to systematically measure accuracy, precision, recall, robustness, and model improvements across diverse datasets and real-world conditions. The Applied Scientist will drive experimentation, dataset and error analysis, model evaluation, and iterative improvements to NER and classification quality, while partnering closely with engineering and product teams to translate scientific advances into scalable capabilities within Microsoft Purview. 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. Responsibilities <ul><li>Design, develop, and deploy AI/ML systems end-to-end, covering data ingestion, feature engineering, model training, evaluation, and production integration.</li><li>Build and optimize Generative AI and LLM-based systems, including agentic workflows, prompt engineering, RAG, and fine-tuning.</li><li>Write production-grade code in Python, C#, etc., with emphasis on scalability, performance, security, testability, and maintainability.</li><li>Partner with engineering, product management, and applied science teams to translate customer/business requirements into robust technical solutions.</li><li>Ship and operate large-scale AI services in the cloud, owning reliability, latency, throughput, accuracy, and cost efficiency.</li><li>Define and execute model evaluation strategies, including offline experimentation, online monitoring, drift detection, bias analysis, and feedback loops.</li><li>Implement MLOps practices, including model CI/CD, versioning, rollout strategies, observability, and live-site monitoring. -</li><li>Apply Responsible AI principles covering privacy, security, explainability, fairness, and compliance throughout development and deployment.</li><li>Stay current with advances in GenAI, LLM frameworks, and ML infrastructure, and evaluate their applicability to enterprise security scenarios. </li></ul> Qualifications <ul><li>Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field. </li><li>5+ years overall experience, including hands-on model development experience and writing production-quality code</li><li>Solid understanding of ML fundamentals, model evaluation, experimentation, and performance trade-offs.</li><li>Experience building or operationalizing LLM / Generative AI systems, including RAG, prompt engineering, or agent-based architectures.</li><li> Ability to collaborate across disciplines and operate autonomously at senior IC scope. </li></ul> This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. <hr> 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 <a href="https://careers.microsoft.com/v2/global/en/accessibility.html">requesting accommodations.</a>