New 
Senior Applied Scientist
 Microsoft | |
 United States, Washington, Redmond | |
 Oct 22, 2025 | |
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OverviewMicrosoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world. Microsoft's Azure Data engineering team is leading the transformation of analytics in the world of data with products like databases, data integration, big data analytics, messaging & real-time analytics, and business intelligence. The products our portfolio include Microsoft Fabric, Azure SQL DB, Azure Cosmos DB, Azure PostgreSQL, Azure Data Factory, Azure Synapse Analytics, Azure Service Bus, Azure Event Grid, and Power BI. Our mission is tobuild the data platform for the age of AI, powering a new class of data-first applications and driving a data culture. Within the Microsoft Fabric product pillar, the RealTime Intelligence (RTI) team is hiring a Principal Applied Scientist to lead realtime ML that powers agentic workflows on live operational data, driving capabilities such as autonomous agents, anomaly detection at scale, and decisioning that closes the loop from detection to action. What makes RTI unique is its deep integration across Fabric's realtime surfaces (e.g., KQL/RT dashboards, streaming pipelines, and Data Activator), a humanintheloop design for trustworthy detections, and shared ML components that let us ship science rapidly across multiple experiences. In this role, you'll partner closely with engineering to architect lowlatency inference, rigorous evaluation, and production monitoring for ML/LLM systems, owning the science from research through deployment and continuous improvement. We do not just value differences or different perspectives. We seek them out and invite them in so we can tap into the collective power of everyone in the company. As a result, our customers are better served.
ResponsibilitiesLead endtoend science for realtime ML/LLM systems that power agentic workflows on streaming data: designing reliable, lowlatency inference and partnering with engineering for robust deployment and operations.Advance anomaly detection and causal insights across RTI surfaces by driving model selection, humanintheloop feedback, and measurable lifts in precision/recall and timetodetection. Establish rigorous evaluation and reliability practices: from offline metrics and online experiments to production monitoring and guardrails, balancing quality with cost/latency at scale. Collaborate with PM, Engineering, and UX to translate research into customervisible scenarios and outcomes, iterating quickly based on telemetry and user feedback. Provide technical leadership and mentorship within the applied science community, fostering inclusive, responsibleAI practices and influencing roadmap and crossteam strategy. Embody our culture and values | |