We're building the next-generation Grounding Service that powers the latest AI applications—chat assistants, copilots, and autonomous agents—with factual, cited, and trustworthy responses. Our platform stitches together retrieval, reasoning, and real-time data so that large language models stay anchored to enterprise knowledge, the public web, and proprietary tools.
We're looking for a Senior Applied Scientist to lead end-to-end science for grounding: inventing retrieval and attribution methods, defining factuality/faithfulness metrics, and shipping production models and APIs that scale to billions of queries. You'll partner closely with engineering, product, research, and customers to deliver fast, reliable, and explainable answers with source citations across a diverse set of domains and modalities.
As a team, we value curiosity, pragmatic rigor, and inclusive collaboration. We believe great systems emerge when scientists and engineers co-design metrics, models, and infrastructure—and when we obsess over customer impact, privacy, and safety.
Responsibilities
Owns the science roadmap for grounding—including retrieval, re-ranking, attribution, and reasoning—driving initiatives from problem framing to production impact.
Designs and evolves state-of-the-art retrieval and RAG orchestration across documents, tables, code, and images.
Builds citation and provenance systems (e.g., passage highlighting, quote-level alignment, confidence scoring) to reduce hallucinations and increase user trust.
Leads experimentation and evaluation using A/B testing, interleaving, NDCG, MRR, precision/recall, and calibration curves to guide measurable trade-offs.
Advances tool-augmented grounding through schema-aware retrieval, function calling, knowledge graph joins, and real-time connectors to databases, cloud object stores, search indexes, and the web.
Partners with platform engineering to productionize models with scalable inference, embedding services, feature stores, caching, and privacy-compliant multi-tenant systems.
Nurtures collaborative relationships with product and business leaders across Microsoft, influencing strategic decisions and driving business impact through technology.
Authors white papers, contributes to internal tools and services, and may publish research to generate intellectual property.
Bridges the gap between researchers (e.g., Microsoft Research) and development teams, applying long-term research to solve immediate product needs.
Leads high-stakes negotiations to ensure cutting-edge technologies are applied practically and effectively.
Identifies and solves significant business problems using novel, scalable, and data-driven solutions.
Shapes the direction of Microsoft and the broader industry through pioneering product and tooling work.
Mentors applied scientists and data scientists, establishing best practices in experimentation, error analysis, and incident review.
Collaborates cross-functionally with PMs, research, infrastructure, and security teams to align on milestones, SLAs, and safety protocols.
Communicates clearly through design documentation, progress updates, and presentations to executives and customers.
Contributes to ethics and privacy policies, identifies bias in product development, and proposes mitigation strategies.
Qualifications
Required Qualifications:
Bachelor’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 Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
OR equivalent experience.
Minimum of 4 years of hands-on experience designing and building search, retrieval, or ranking systems.
Proven track record of shipping LLM-powered or Retrieval-Augmented Generation (RAG) systems into production environments.
Solid coding skills and solid foundation in machine learning, with the ability to implement and optimize models effectively.
Demonstrated ability to lead through ambiguity, make principled trade-offs, and deliver measurable impact in cross-functional, fast-paced settings.
Preferred Qualifications:
Master’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 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.
Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL.
Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL).
Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects.
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