Overview
Microsoft Research Internships provide a dynamic environment for building a career in research. Research Interns join a network of world-class research labs led by globally recognized scientists and engineers, working across a range of scientific and technical disciplines.
This role is specifically for a Microsoft Teams Research Intern — you’ll work on advanced research that supports innovation in collaboration and communication technologies. Projects may involve exploring cutting-edge areas such as Artificial Intelligence (AI)-driven productivity tools, conversation intelligence, meeting efficiency, and user experience enhancements, all while ensuring privacy, security, and accessibility remain central to solutions.
You will collaborate with multidisciplinary teams, applying research methods to address real-world challenges in digital collaboration, and contribute to shaping the future of how people connect and work together through Microsoft Teams.
Responsibilities
Research Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world’s best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer.
Qualifications
Required Qualifications
- Currently enrolled in a PhD program in Computer Science, Mathematics, or a related STEM field.
Other Requirements
- Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.
- In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.
Preferred Qualifications
- Creative & Innovative Thinking – Proven ability to think unconventionally and develop novel solutions to complex problems.
- Active Research Engagement – Actively conducting research in at least one of the following areas:
- Collaborative or Multi-party AI Agents (e.g., AI teammates).
- Multi-Agent Simulations (e.g., evaluating or training AI agents in collaborative settings).
- Tool-use and multi-turn reinforcement learning.
- Learning from user interactions or human feedback in real-world environments.
- Synthetic data generation for LLM evaluation and fine-tuning.
- Rubric-based LLM graders for reward models.
- Evaluation and benchmarking of LLM agents.
- Agent capabilities (e.g., memory, goal management, inter-agent conversations).
- Model fine-tuning.
- Collaboration Skills – Ability to work effectively with a mentor and cross-functional teams within Microsoft on research projects, demonstrating proficient interpersonal, cross-group, and cross-cultural communication skills.
- Research Excellence – Proven track record of independent research, ability to work towards well-defined milestones, and commitment to meeting project deadlines.
- Special Focus Areas – Candidates with the following interests and experience will be prioritized:
- Social Computing & Model Training/Behavior/Evaluation – Experience in developing and evaluating social agents, with an interest in solving the “presence” problem in a scalable way. This includes:
- Training small models to filter events.
- Building procedural systems for sleeping/debouncing.
- Evaluating whether agent interactions feel natural and understandable.
- Memory-Related Research – Expertise in areas such as memory consolidation, Graph RAG, or knowledge graphs, with bonus consideration for experience in memory forgetting or group personalization.
