Microsoft Research Intern - AI-driven System Database Design 

Canada, British Columbia, Vancouver 

193415088

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

• Proficient analytical and problem-solving skills and communication skills, both written and verbal.
• Ability to work independently and collaboratively in a dynamic research environment.
• AI-driven System Design
• Proficient in machine learning concepts and models.
• Research on how to use ML method to validate and verify the quality of the paragraph of text.
• Hands-on experience with machine learning frameworks such as PyTorch and programming skills on C/C++ or Python.
• Passionate about solving real-world large-scale infrastructure problems with AI Models. Experience with AI infrastructure or LLM would be a plus.
• AI-driven Database Design
• Proficient understanding of machine learning or database concepts.
• Research expertise with database, data processing, data storage, vector indexing, and AI modeling is a plus.
• AI Modeling
• Proficient understanding of machine learning concepts.
• Research expertise with AI modeling is a plus.
• Deep Knowledge of AI model design, architecture, and application.

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.