Research Intern - Agent Systems for AI Infrastructure
Overview
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 Asia Vancouver, you will be at the forefront of developing and implementing cutting-edge agent systems for AI infrastructure. This position is perfect for those passionate about the future of agent systems and AI-driven design. You will work alongside a team of top-tier researchers and engineers in Vancouver, Canada, and Redmond, US, to create LLM-based agent systems that will drive the next generation of AI infrastructure, enhancing its efficiency and effectiveness.
Qualifications
Required Qualifications
- Currently enrolled in a bachelor'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 in programming languages such as C/C++ or Python.
- Familiar with fundamental concepts related to LLM, prompt engineering, and LLM-based agents. Experience in AI for systems and/or systems for AI.
- Passionate about addressing real-world large-scale infrastructure problems using AI models. Experience with AI infrastructure or LLM would be a plus.
- Proficient analytical and problem-solving skills and communication skills, both written and verbal.
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: https://careers.microsoft.com/v2/global/en/canada-pay-information.html
Microsoft accepts applications and processes offers for these roles on an ongoing basis.
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.
Additional Responsibilities
- Development and Implementation: Design LLM-based agent systems for AI infrastructure. Implement prototypes and conduct experiments to test and validate them.
- Research and Analysis: Conduct thorough research on emerging trends in agent systems for 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 agent 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.