Research Intern

1 week ago

Vancouver, Canada Microsoft Canada Full time

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.

**Qualifications**:
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.
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.
**AI-Driven Optimization**:
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.

**Responsibilities**:
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.

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