Research Intern

1 month 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.
Proficient understanding of AI and machine learning concepts, especially in relation to hardware infrastructure.
Experience in simulation modeling and software development (e.g., Python, C++, MATLAB).
Research expertise with AI hardware components such as GPUs, TPUs, and neuromorphic chips. Hardware RTL development (SystemVerilog, Chisel, Bluespec) is a plus.
Experience with machine learning frameworks (TensorFlow, PyTorch).
Deep Knowledge of hardware design and architecture.
Prior research or project experience in AI or hardware simulation.
Intern - MSR- The typical base pay range for this role across Canada is CAD $8,700 - CAD $9,700 per month.

**Responsibilities**:
**As a Research Intern for this position, you will**:
**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.

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