Principal Applied Scientist @ Microsoft
placeRedmond, Washington, United States
attach_money $200,000
businessOn Site
scheduleFull-time
Posted 23 days ago
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Interview
Email Hiring Manager
****** @microsoft.com
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Job Details
Principal Applied Scientist
The Microsoft Bing Web Data Platform’s Document Understanding team seeks a Principal Applied Scientist in Redmond, WA. You will build cutting-edge solutions for document understanding on a web scale, working on some of the largest datasets.
About the Role
This role is focused on delivering the world’s freshest, richest, and clean semantic understanding of web document representation using large-scale machine learning and LLMs. You will lead projects from ideation and experimentation to deployment, collaborating across teams to translate business problems into machine learning solutions.
Key Responsibilities
- Develop and evaluate NLP, information retrieval, and related ML models.
- Define metrics, oversee data labeling, and optimize model performance.
- Build scalable, production-ready systems with strong software engineering fundamentals.
- Collaborate with cross-functional teams to drive real-world impact.
- Advance Bing and other Microsoft experiences through cutting-edge ML and LLM technologies.
Key Skills/Competency
Principal Applied Scientist, NLP, Information Retrieval, Machine Learning, LLM, Data Labeling, Model Development, Software Engineering, Scalable Systems, Collaboration
How to Get Hired at Microsoft
🎯 Tips for Getting Hired
- Research Microsoft’s culture: Study their mission, values, and innovations.
- Tailor your resume: Emphasize ML and data expertise.
- Highlight key projects: Demonstrate experience with NLP and LLMs.
- Practice technical interviews: Focus on ML algorithms and scalable systems.
📝 Interview Preparation Advice
Technical Preparation
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Review NLP and ML algorithm fundamentals.
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Hands-on practice with large language models.
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Study scalable system design and engineering.
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Familiarize with production ML deployment.
Behavioral Questions
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Describe a challenging project in ML.
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Explain teamwork in cross-functional settings.
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Showcase leadership in problem-solving.
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Detail handling tight project deadlines.
Frequently Asked Questions
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