Microsoft Research Intern - AI HW/SW Co-design 

United States, Washington 

810059601

Strategic Planning and Architecture

AI System Architecture (ASA) within SPARC has the mission to co-design system architecture of Azure AI cloud keeping in pace with evolving landscape of Neural Network models.

• Currently pursuing a master's or PhD degree in Electrical Engineering, Computer Engineering or Computer Science.
• At least 1 year experience on working with deep learning hardware architectures.

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.

• Ability to understand prevailing Neural Network Architectures.
• Proficient understanding of AI algorithms and hardware architectures.
• Experience with hardware description languages (HDLs) such as VHDL or Verilog.
• Proficiency in programming languages such as Python, C++, or CUDA.
• Familiarity with AI frameworks such as TensorFlow, PyTorch, or Caffe.
• Knowledge of machine learning accelerators and hardware/software co-design methodologies.
• Previous research experience or internships in related fields.

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:

• Benchmark prevailing Neural Network architectures to understand Hardware/system characteristics.
• Participate in performance analysis and optimization of AI workloads.
• Come up with AI algorithms, system or hardware architecture innovations to drive efficiencies.
• Assist in conducting research on AI hardware/software co-design methodologies.
• Develop and implement co-design techniques to optimize AI performance.
• Prototype the ideas to demonstrate the optimizations.
• Write white papers and research publications.