Core Search and AI team (Bing) is looking for people who want to build the next generation of search using advanced AI technologies, especially large language models, at scale. We are responsible for the largest machine learning models at Microsoft by volume and take pride in being the first in the world to solve many practical AI at Scale challenges. Our work spans a very large scope of scenarios including delivering high quality search results from a massive document corpus, query and document understanding, retrieval and reranking model for search results optimization, as well as AI search grounding, etc.

You will master one or more subareas and build expertise across a broad research landscape, including advanced research methodologies and applied techniques.

You’ll develop deep knowledge of a service, platform, or domain, and identify product opportunities by sharing emerging industry trends and applied technologies.

You will review business requirements and incorporate research insights to meet strategic goals.

You’ll provide direction on the types of data needed to solve complex problems and apply deep subject-matter expertise to drive measurable business impact.

You will support the onboarding of junior team members and help develop academic collaborators into effective contributors within multidisciplinary teams.

You’ll identify promising research talent, engage with the academic community, and strengthen Microsoft’s long-term recruiting pipeline.

You will document ongoing work, experimental results, and research findings to promote transparency and innovation.

You’ll also apply your understanding of fairness and bias to help shape ethics and privacy policies related to research processes and data collection.

You will advance LLM post-training and alignment techniques by designing, implementing, and evaluating novel methods that improve reasoning quality, safety, controllability, and factual grounding across large-scale models.

You will develop next-generation search capabilities by building and optimizing retrieval, ranking, and relevance systems that integrate deeply with LLM-powered experiences.

You will architect and refine RAG pipelines that enhance retrieval fidelity, reduce hallucinations, and deliver more context-aware, user-aligned responses in production environments.

You will translate research into production by running experiments, analyzing results, and collaborating with engineering partners to deploy scalable, reliable model improvements.

You will drive scientific rigor through hypothesis-driven experimentation, reproducible methodologies, and clear documentation of findings, insights, and model behaviors.

You will collaborate across disciplines—including research, engineering, product, and design—to shape long-term strategy for search, alignment, and retrieval-augmented systems.

You will monitor and evaluate model performance using quantitative metrics, qualitative assessments, and user-centric evaluation frameworks to ensure continuous improvement.

You will contribute to a culture of innovation by sharing learnings, mentoring peers, and participating in internal research discussions, reviews, and technical deep dives.