Research Scientist Intern, Responsible AI (PhD/Masters)

This internship lies at the intersection of responsible AI, machine learning and recommender systems. In the context of multi-stakeholders recommender systems, the goal is to analyze the long-term impact of interventions aimed at ensuring fairness of exposure of content creators. This will be done by designing algorithms to learn in nonstationary environments, where the environments are based on user and creator behavior models from the recommender systems or economics literatures. This internship follows our work on fairness of exposure.Internships will be awarded on a rolling basis and candidates are encouraged to apply early.

Research Scientist Intern, Responsible AI (PhD/Masters) Responsibilities:

• Brainstorm with research mentors, review literature and existing solutions of a challenging real-world research problem.
• Develop novel solutions, implement prototypes and perform extensive experiments to test the proposed solutions in meaningful benchmarks and metrics, analyze the results and verify the conclusions.
• Communicate and discuss with team members about various aspects of the project. This includes answering questions, addressing concerns, improving solutions.
• Draft and refine research publications.

Minimum Qualifications:

• Currently has, or is in the process of obtaining a PhD or Masters degree.
• Experience in Python, C++ or other related languages.
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorisation during employment.

Preferred Qualifications:

• Strong publication records in specific fields (e.g., high impact research works published on top-tier conferences, popular github repositories, etc).
• Intent to return to degree-program after the completion of the internship/co-op.
• Extensive experience solving analytical problems using quantitative approaches.
• Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
• Ability to communicate complex research in a clear, precise, and actionable manner.
• Experience building large-scale machine learning systems and training with large datasets.

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