Microsoft Research Intern - AI-Driven System Design Optimization 

United States, Washington 

879216318

• 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.

• 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.
• Distributed, parallel, or GPU acceleration for modern applications.

AI-Driven Optimization:

• Experience with LLMs and their applications in dynamic and complex environments.
• Knowledge of multi-agent systems and Active Learning.
• Knowledge of SOTA Prompt Engineering techniques.
• Background in Optimization (e.g., Black-Box Optimization).

Applied Sciences IC2 : The base pay range for this internship is USD $5,460 -$10,680 per month.Applied Sciences IC3 : The base pay range for this internship is USD $6,550 -$12,880 per month.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• 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 large language model (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.