Applied Scientist II and Senior: Microsoft AI Development Acceleration Program, Cambridge

Microsoft•Cambridge, MA

11h•$102,100 - $261,000•Onsite

About The Position

This opportunity is part of the Microsoft AI Development Acceleration Program (MAIDAP) in Cambridge, designed to foster community, passion, and excellence. Microsoft is transforming Azure into a global AI supercomputer to enable the responsible development of cutting-edge foundational AI, including large language models (LLMs). The MAIDAP program aims to develop the next generation of AI leaders by having participants work in interdisciplinary teams to provide AI as a service to engineering teams across Microsoft and solve challenging problems. Participants will receive mentorship, exposure to leaders, and access to AI experts. Upon program completion, there's an opportunity to join sponsoring product teams. MAIDAP Applied Scientists will leverage or instantiate novel AI technologies into production, acting as a bridge between research and development. They will collaborate with software engineers, product managers, and other product and technology teams across Microsoft to drive innovation from theory to reality. A PhD is preferred for this role, demonstrating advanced proficiency in scientific methodology, experimental design, and research practices. Microsoft's mission is to empower every person and organization to achieve more, fostering a culture of respect, integrity, accountability, and inclusion with a growth mindset.

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
OR equivalent experience.
Candidates must be available to start full-time in July 2026.

Nice To Haves

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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.
1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
A publication record in any of the following or related modeling paradigms: LLMs/SLMs/multi-modal models/probabilistic graphical models/Bayesian networks/deep learning/reinforcement learning/time series/active learning/fair and interpretable AI/optimization for machine learning.
Experience in any of the deep learning frameworks, systems, or big-data application solutions, along with application experience in language, speech, vision, graphics, gaming, or recommendation.
2+ years experience and proven knowledge in Python/R/Scala or similar.
Energized by creating AI solutions and the prospect of working on a wide variety of datasets and AI applications, across many products and engineering teams.

Responsibilities

Research, develop, and lead the implementation of AI solutions in application projects for Microsoft’s products and services.
Select and apply appropriate statistical and machine learning techniques to large-scale, high-dimensional data.
Stay current with the latest research and technology and communicate your knowledge throughout the organization.
Take responsibility for preparing data for analysis, reviewing data preparation/ETL code, and providing critical feedback on issues of data integrity.
Share knowledge by clearly articulating results and ideas to customers, managers, and key decision makers.
Patent and publish relevant IP and scientific research.