Principal Applied Scientist - Copilot

Microsoft•Redmond, WA

About The Position

Come build the core of Microsoft Copilot for enterprise with Microsoft Turing Team, where you'll join a collaborative group of applied scientists and engineers pushing the frontier of large language models (LLMs) to improve the productivity of hundreds of millions of users around the world. Our team is responsible for the core systems that power Microsoft 365 Copilot Chat delivering intelligence, quality, and transformative new features. We work at the intersection of research and engineering—advancing orchestrator reasoning, training next-generation models, and shipping impactful, model-driven experiences in Microsoft 365 Copilot.

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.
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

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.
5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
5+ years experience conducting research as part of a research program (in academic or industry settings).
3+ years experience developing and deploying live production systems, as part of a product team.
3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
4+ years of experience with end-to-end in a challenging technical problem domain (plan, design, execution, continuous release, and service operation).
4+ years experience with shipping internet scale, low latency and high throughput systems AI products.
4+ years experience with customer/End-result/Metrics driven in design and development.
Publications at top conferences like ACL (Association for Computational Linguistics), EMNLP (Empirical Methods in Natural Language Processing), SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining), AAAI (Association for the Advancement of Artificial Intelligence), WSDM (Web Search and Data Mining), COLING (International Conference on Computational Linguistics), WWW (World Wide Web Conference), NIPS (Neural Information Processing Systems), ICASSP (International Conference on Acoustics, Speech, and Signal Processing), etc.

Responsibilities

Independently scope, design, and deliver applied ML/LLM solutions for AI product problems.
Conduct research and development to push the boundaries of model training, evaluation, and quality assessment for AI solutions.
Post-train LLMs for enterprise scenarios M365 Copilot and on tenant data to enable task-specific agents and solutions within the enterprise ecosystem.
Collaborate with engineers and researchers to advance deep learning, natural language processing (NLP), multimodal models, and model optimization techniques.
Measure, analyze, and report on model performance, quality, and impact using predictive analytics and statistical methodologies.