Senior Applied Scientist

Microsoft • Redmond, WA

1d • $119,800 - $261,000

About The Position

The Senior Applied Scientist will help 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 role spans the retrieval stack, including understanding user requests, query formulation, lexical and semantic retrieval, and determining when sufficient evidence has been found. Language models are trained and fine-tuned using supervised and reinforcement learning, and improvements are evaluated offline and validated through online experiments.

The scientist will own open-ended search and grounding problems end to end, from problem framing and data development through modeling, evaluation and deployment, while contributing to 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 scientific research and engineering, with a focus on robust systems, user trust, performance and clear outcomes.

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 through 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 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 ability to explain complex results to technical and non-technical audiences.

Responsibilities

• Advance AI information search, including understanding user needs, turning requests into precise queries, choosing where to search and determining when enough 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 using supervised fine-tuning and reinforcement learning to improve quality and speed.
• Design evaluation methods and reward signals that reflect 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 develop model, data and system fixes for production.
• Partner with engineering, product, ranking and research teams to move techniques from experiments into production across languages and markets.
• Mentor scientists and engineers, promote experimental rigor, and communicate results and trade-offs to technical and non-technical audiences.

What This Job Offers

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