Senior Applied Scientist

Microsoft • Redmond, WA

1d • $119,800 - $234,700

About The Position

The Senior Applied Scientist will develop search and grounding technology that finds relevant information across email, documents, meetings, chats and people, and connects AI-generated answers to supporting sources. The work spans the retrieval stack, including understanding user intent, translating everyday requests into precise queries, combining lexical and semantic retrieval, and determining when sufficient evidence has been found. Language models are used throughout and fine-tuned with supervised and reinforcement learning. Results are evaluated offline for relevance, accuracy, citation quality and speed, then validated through online experiments.

The scientist will own open-ended search and grounding problems end to end, from problem framing and data creation through modeling, evaluation and deployment, and will help set the team’s scientific direction. Work takes place in a privacy-first environment where models are trained and evaluated without direct access to customer data, making synthetic data and thoughtful evaluation design important. The work improves search in Outlook, Teams, SharePoint and OneDrive and grounds Microsoft 365 Copilot answers. The role combines science and engineering, from production coding to training language models.

Requirements

• Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 4+ years of related experience, or a master's degree in one of these fields and 3+ years of related experience, or a doctorate in one of these fields and 1+ year of related experience, or equivalent experience.
• Ability to meet Microsoft, customer and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Nice To Haves

• Master's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 6+ years of related experience, or a doctorate in one of these fields and 3+ years of related experience, or equivalent experience.
• Solid foundation in information retrieval and search, including query understanding, retrieval, ranking and relevance evaluation.
• Experience building retrieval-augmented generation (RAG), agentic search or other systems that ground language model outputs in external data.
• Hands-on experience training or fine-tuning language models, such as with supervised fine-tuning or reinforcement learning, and shipping results to production.
• Experience designing evaluation metrics, test sets or model-based judges for search or answer quality.
• Solid experimentation skills, including statistical significance testing and A/B experiments, and the ability to turn results into product decisions.
• Experience working with sensitive data under privacy constraints, including synthetic data generation.
• Proficiency in Python and modern machine learning frameworks such as PyTorch, and experience working with large-scale data.
• A track record of publications, patents or equivalent applied impact, and the ability to explain complex results to technical and non-technical audiences.

Responsibilities

• Advance how AI systems search for information by understanding user needs, translating requests into precise queries, choosing where to search, and determining when sufficient evidence has been found.
• Improve the quality, coverage and reliability of grounding data so answers are relevant, accurate and supported by cited sources.
• Train and fine-tune language models for search and grounding tasks using supervised fine-tuning and reinforcement learning to improve quality and speed.
• Design evaluation methods and reward signals that reflect user priorities such as relevance, accuracy, trustworthy citations and efficiency.
• Build training and evaluation data in privacy-preserving ways, including through synthetic data generation.
• Define offline and online metrics for search and answer quality, and ensure offline improvements predict production user experience.
• Investigate real-world failures, identify root causes, and deliver model, data and system fixes.
• Partner with engineering, product, ranking and research teams to bring techniques from experiments into production across languages and markets.
• Mentor scientists and engineers, promote rigorous experimentation, and clearly communicate results and trade-offs to technical and non-technical audiences.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior