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Senior Applied Scientist at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

19 Jan, 26

Salary

0.0

Posted On

21 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Real-Time Analytics, Data Integration, Anomaly Detection, Low-Latency Inference, Production Monitoring, Data Culture, Cloud Computing, AI, Data Engineering, Human-In-The-Loop Design, Big Data, Business Intelligence, Streaming Pipelines, Decision Making

Industry

Software Development

Description

Microsoft 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 to build 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 Real‑Time Intelligence (RTI) team is hiring a Principal Applied Scientist to lead real‑time 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 real‑time surfaces (e.g., KQL/RT dashboards, streaming pipelines, and Data Activator), a human‑in‑the‑loop 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 low‑latency 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.

Responsibilities

Lead real-time machine learning initiatives that enhance operational data workflows. Collaborate with engineering teams to ensure effective deployment and continuous improvement of ML systems.