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Microsoft

Hybrid

Quick summary

Work type: Hybrid
Location: —
Salary: $139,900–$274,800 / yr
Posted: 61 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $180k

This role $207k

$101k most similar roles pay here $293k

This role pays more than 78% of similar roles. Most pay $152,150–$207,350 — the shaded band above. At the midpoint, this role pays about $207k versus about $180k for comparable roles.

Based on 239 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 1577 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 1405 roles with salary data.

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At a glance

TL;DR · | Microsoft Careers

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As a Principal Applied Scientist on Microsoft’s Advertising Platform team, you will lead the development of Copilot Chat Assistant, an AI-driven tool that empowers advertisers by orchestrating multiple Large Language Models (LLMs) using agentic frameworks and vector databases. Your daily tasks include designing robust retrieval pipelines for precise answers, advancing prompt design to enhance accuracy and efficiency, and collaborating with engineering teams to ship features while ensuring reliability through telemetry and A/B testing. You will also mentor team members and establish best practices, all while measuring the impact of your work on top-line revenue metrics. Ideal candidates have extensive experience in deploying live production systems, leveraging LLMs, agentic frameworks, RAG, and vector databases to solve complex business problems at scale.

Skills

LLM Agentic_Frameworks Semantic_Kernel RAG Vector_Databases MCP Python Kubernetes CI/CD Prometheus Grafana PostgreSQL AWS Azure

What you'll do

Own the science roadmap for Copilot Chat Assistant from concept to reliable production deployment.
Design multi-agent reasoning systems integrating LLM orchestration and tool use for complex tasks.
Develop robust retrieval pipelines using RAG and vector stores for accurate, contextually relevant responses.
Enhance prompt design techniques to improve system accuracy, speed, and cost-effectiveness.
Collaborate with engineering teams to ship features, ensuring quality through telemetry and A/B testing.
Mentor team members and establish best practices for scientific rigor in product development.
Define success metrics and iterate based on data analysis and customer feedback.

What we're looking for

Extensive experience (5+ years) in developing and deploying live production systems.
Deep expertise in designing and implementing multi-agent reasoning systems with LLM orchestration.
Proven track record of building robust retrieval pipelines using RAG and vector databases.
Strong background in prompt/program design for accuracy, latency, and cost efficiency.
Experience in leading the development lifecycle from ideation to shipping complex products.

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