Microsoft Research Intern - AI Infrastructure 

Canada, British Columbia, Vancouver 

714180436

Required Qualifications:

• Currently enrolled in abachelor's, master's, or PhD 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

• Proficientanalytical and problem-solving skillsandcommunication skills, both written and verbal.
• Ability to work independently and collaboratively in a dynamic research environment.
• DNN programming and optimization experience.
• Programming ability in Pytorch, C++, or Python, or experience conducting experiments and writing papers.
• Distributed, parallel, or GPU acceleration for modern applications.

Find additional pay information here:

• Development and Implementation: Design and develop AI-driven AI infrastructure. Implement prototypes and conduct simulations to test and validate them.
• Research and Analysis: Conduct thorough research on emerging trends in AI software and hardware infrastructure.
• 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.