Principal Applied Scientist

Microsoft•Redmond, WA

2d•Hybrid

About The Position

The Microsoft Content team powers AI-driven experiences for more than 1B users across Copilot, Bing, Edge, Windows, and Xbox. We are seeking a Principal Applied Scientist to define and develop the next generation of intelligent, large-scale content platforms. In this role you will be responsible for modeling, experimentation, product impact, and technical leadership. The ideal candidate will have deep expertise in large language models, information retrieval, ranking, grounding, search systems, and agentic AI. They will work on improving how AI systems retrieve information, reason over context, maintain multi-turn conversations, use tools, rank candidate responses or actions, and produce reliable outputs grounded in trusted data. A hands-on experience tuning and improving models at scale, including techniques such as supervised fine-tuning, preference optimization, model distillation, data curation, evaluation design, and large-scale experimentation will be needed. The individual will be expected to lead complex scientific workstreams, influence product direction, and partner closely with engineering, research, and product teams. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
10+ industry experience building, evaluating, and deploying machine learning models or AI systems in production.
Deep expertise in at least two of the following areas: search, information retrieval, learning-to-rank, recommendation systems, grounded generation, LLM-based ranking, retrieval-augmented generation, conversational AI, or agentic AI systems.
Experience with offline and online evaluation, including relevance metrics, A/B experimentation, human evaluation, model diagnostics, and production quality monitoring.
Experience working with large-scale datasets, production ML pipelines, distributed training or inference systems, and cross-functional engineering teams.
Demonstrated ability to lead ambiguous technical projects as a senior individual contributor and influence product, engineering, and science direction.
Communication skills, with the ability to explain scientific tradeoffs clearly to technical and non-technical stakeholders.

Nice To Haves

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
Experience with production-scale LLM systems, agent frameworks, search engines, ranking systems, or retrieval-augmented generation systems.
Experience improving multi-turn AI assistant or agent experiences in real products.
Experience with tool-using agents, planning systems, memory, personalization, source ranking, or enterprise search.
Experience building evaluation frameworks for factuality, grounding, hallucination, relevance, safety, and task completion.
Experience with large-scale experimentation platforms, A/B testing, human evaluation, and model quality monitoring.
Experience collaborating with product teams to translate model improvements into measurable customer impact.
Experience with publication record, patents, open-source contributions, or demonstrated technical leadership in applied AI, IR, NLP, or ML systems.

Responsibilities

Lead applied science for grounding, search, retrieval, and ranking systems that power Microsoft AI and agent experiences across large-scale content surfaces.
Develop and improve ranking and reranking models for search results, retrieved passages, source selection, answer candidates, tool choices, and agent actions.
Build grounding systems that help AI agents generate reliable, source-backed answers using trusted documents, web content, enterprise data, tool outputs, and user context.
Improve end-to-end search and retrieval quality, including query understanding, semantic and hybrid retrieval, freshness, relevance, source quality, personalization, and latency-aware ranking.
Advance multi-turn agent experiences by improving context understanding, tool use, task completion, clarification behavior, planning, and recovery from errors.
Tune and optimize models for grounded and agentic behavior using methods such as supervised fine-tuning, preference optimization, reward modeling, distillation, synthetic data generation, and scalable experimentation.
Define and own evaluation methods and success metrics for ranking quality, retrieval quality, groundedness, factuality, citation correctness, hallucination reduction, task success, user satisfaction, latency, and cost.
Partner with engineering, product, research, and design teams to ship science improvements into production, influence architecture, mentor scientists and engineers, and drive high-impact initiatives from ambiguity to measurable product impact.