Principal Applied Scientist

Collaborating and coordinating with people in a range of roles, including researchers, engineers, product managers, designers, and other key product stakeholders. Serving as a bridge between research and product. Teaching, guiding, and tutoring colleagues without research backgrounds in state-of-the-art techniques and research best practices. Many of the things you will try should lead to unexpected outcomes. You must be comfortable learning from experience, developing new hypotheses, and iterating. Sharing your research with others via a range of means, including publication, to enable others to build on your work and to contribute to the understanding of our products as cutting-edge and science driven. Effectively communicating with product leaders (and often MSR researchers) throughout the planning and execution of applied research projects, which often involves learning and teaching complex concepts. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND extensive years of 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 proven years of experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND proven years of related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Extensive years of experience developing and deploying AI/ML products or systems at multiple points in the product cycle from ideation to shipping Proven years of experience with transformer-based models (e.g., GPT, Llama) Ability to apply research, preferring the large-scale application of new ideas over theory and prototypes. Demonstrated collaboration and communication skills, both within-team and cross-team, and experience working through problems in ambiguous environments adapting to new research challenges as technology develops. Proficiency in Python for data science workflows, leveraging libraries such as NumPy, Pandas, scikit-learn, Matplotlib, and Jupyter for data analysis, experimentation, and rapid prototyping Exposure to Deep Neural Network training and inference and experience in one or more deep learning frameworks (PyTorch, TensorFlow, etc.). Experience with Agent based architectures, frameworks and technologies (such as MCP, A2A protocol, Langchain, Azure AI Foundry) Experienced in web application development frameworks, such as React Experience delivering a Cloud SaaS product on Azure, AWS or GCP A record of publication in top-tier scientific venues (ongoing or in the recent past) (e.g., NeurIPS, ICML, ICLR, ACL, NAACL, EMNLP, KDD, WWW, CHI, PNAS, Nature) These requirements include but are not limited to the following specialized security screenings: