Research Intern - AI-Driven System Design and Optimization

Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers, who pursue innovation in a range of scientific and technical disciplines to help solve complex challenges in diverse fields, including computing, healthcare, economics, and the environment.

As a Research Intern at Microsoft Research, you will be at the forefront of developing and implementing cutting-edge Artificial Intelligence (AI)-driven AI infrastructure. This role is ideal for candidates who are passionate about AI software and hardware development. You will collaborate with a team of world-class researchers and engineers in Vancouver, Canada, and Redmond, Washington to create the next generation of AI infrastructure that enhances the efficiency and effectiveness of AI.

Required Qualifications

• Currently enrolled in a bachelor's, master's, or Ph.D. program in Computer Science, Electrical Engineering, Machine learning, Mathematics, or a related 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

• Proficient analytical and problem-solving skills and communication skills, both written and verbal.
• Ability to work independently and collaboratively in a dynamic research environment.
• Programming ability in C++, or Python, or experience conducting experiments and writing papers.

AI-driven Infrastructure:

• DNN programming and optimization experience.
• Distributed, parallel, or GPU acceleration for modern applications.

AI-Driven Optimization:

• Experience with LLMs and their applications in dynamic and complex environments.
• Knowledge of multi-agent systems and Active Learning.
• Knowledge of SOTA Prompt Engineering techniques.
• Background in Optimization (e.g., Black-Box Optimization).

Intern - MSR- The typical base pay range for this role across Canada is CAD $8,700 - CAD $9,700 per month.

Find Additional Pay information here:

Microsoft accepts applications and processes offers for these roles on an ongoing basis.

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.

Additional Responsibilities

• Development and Implementation: Design and develop AI-driven optimization and systems. Implement prototypes and conduct simulations to test and validate them.
• Research and Analysis: Conduct thorough research on emerging trends in LLM, prompt engineering, and optimization techniques.
• Collaboration: Work closely with cross-functional teams, including hardware engineers, software developers, and data scientists, to integrate your ideas with existing and future AI projects.
• Documentation and Reporting: Prepare detailed documentation of simulations, methodologies, and findings. Present results and insights to team members and stakeholders.
• Innovation and Problem-Solving: Identify challenges and bottlenecks in AI infrastructure and propose innovative solutions.