Graduate Research Intern – School of Computer Science – MLD

at Carnegie Mellon University
Published March 30, 2023
Location Pittsburgh, United States of America
Category Machine Learning  
Job Type Internship  

Description

Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the curious to deliver work that matters, your journey starts here.

The Machine Learning Department is searching for a Graduate Research Intern to join the team. The department is made up of a multi-disciplinary team of faculty and students across several academic departments. Machine Learning is dedicated to furthering the scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision making based on that understanding. This is an excellent opportunity for someone who thrives in an interesting and challenging work environment. This position will entail collecting, assimilating, and analyzing both qualitative and quantitative data from prior academic publications, with the goal of creating a tool that offers a pipeline-aware view of Fairness for Machine Learning to researchers and practitioners. In addition, the intern will create a user interface that allows users to explore the use cases, measures, and mitigation techniques available to investigate specific stages of the ML pipeline for potential biases.

Core Responsibilities:

  • Perform Literature review(s)
  • Collect and analyze data from published articles.
  • Creating a UI that allows users to explore a pipeline-aware view of Fairness in Machine Learning.
  • Other duties as assigned

Inclusion and cultural sensitivity are valued competencies at CMU. Therefore, we are in search of a team member who can effectively interact with a varied population of diverse audiences. We are looking for someone who shares our values and who will support the mission of the university through their work.

This is a great opportunity for someone to work in a creative, dedicated, driven team, in a collaborative environment committed to technical innovation, inclusion, and work-life balance.

Qualifications:

  • Bachelor’s Degree; Master’s Degree preferred
  • Knowledge of Machine Learning, Fairness and Bias in Machine Learning.
  • Sufficient research experience in the field of Machine Learning.
  • Coding and UX Design experience
  • A combination of education and relevant experience from which comparable knowledge is demonstrated may be considered.

Requirements:

  • Successful background check

Are you interested in this exciting opportunity?! Apply today!

CMU’s COVID-19 Vaccination Requirements: As a condition of employment, Carnegie Mellon University requires all staff and faculty working in the United States to be fully vaccinated, including a booster when eligible, against COVID-19. Prior to commencement of employment, new hires in the United States must provide proof of vaccination or obtain an approved exemption. (Exemptions may be requested for medical reasons or for religious or strong moral or ethical conviction.) Staff and faculty must comply with all applicable COVID-19 mitigation requirements. Please see Minimum Requirements to Return to Campus for details regarding the university’s current COVID-19 mitigation requirements.

 

Location

Pittsburgh, PA

Job Function

Researchers

Position Type

Staff – Fixed Term (Fixed Term)

Full Time/Part time

Full time

Pay Basis

Hourly

More Information: 

  • Please visit Why Carnegie Mellonto learn more about becoming part of an institution inspiring innovations that change the world.
  • Click here to view a listing of employee benefits
  • Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.
  • Statement of Assurance