About the job
Research Interns at Microsoft work in a dynamic environment for research careers with a network of world-class research labs. The OneDrive and SharePoint (ODSP) Applied Science team drives AI innovation to transform how users remember, reason, act on their content and collaborate with others via Microsoft products. Research Interns will investigate, propose, implement, and evaluate new approaches within the broader areas of large language models (LLMs), Multimodal AI, Reinforcement Learning, Conversational AI, and AI Agents.
Responsibilities
Conduct experiments and develop novel AI models and algorithms to address complex ODSP scenarios (e.g., intelligent document understanding, search/RAG, recommendation, generative experiences, proactive knowledge mining).
Design rigorous evaluation metrics, methodologies, and validation experiments to measure performance and quality of devised AI solutions and agentic AI workflows.
Conduct user studies to gather qualitative and quantitative insights, ensuring solutions align with user needs and improve real-world experience.
Build datasets (including leveraging privacy-preserving synthetic data techniques) to fine tune and benchmark models for ODSP applications.
Deliver models and algorithms for content understanding, enrichment, and use at scale, across a range of different modalities, including text, images, and video.
Apply your in-depth knowledge, problem-solving skills, and drive to solve new challenges in the field and realize your ideas in products used worldwide.
Present research findings and propose ideas in team discussions; share results through documentation and presentations and contribute to research publications.
Embody Microsoft culture and values.
Qualifications
Minimum
Currently enrolled in a PhD program in Computer Science, Electrical/Computer Engineering, Data Science, Statistics, or related fields.
Preferred
Demonstrated foundation in machine learning and artificial intelligence. Hands-on experience with modern deep learning techniques (e.g., transformer models, large language models). Practical Python coding experience with PyTorch or similar frameworks. Ability to prototype and implement algorithms efficiently. Proficient analytical, problem solving, communication, and collaborative skills. Able to formulate hypotheses, drive experiments, and work effectively in a collaborative environment. Demonstrated research impact through publications or projects in relevant AI domains (e.g., natural language processing, information retrieval, computer vision, multimodal AI, knowledge mining). A track record of applying scientific methods to real-world problems is a plus. Familiarity with LLM training or fine-tuning, particularly using reinforcement learning techniques or AI agent orchestrators. Experience working with large-scale datasets, enterprise content, or content management systems. Experience with privacy-preserving evaluation methods or synthetic data generation is highly desirable.