Research Scientist Intern,AI Core Machine Learning (PhD)

Meta AI (FAIR), a world-class research lab, is seeking Research Interns to join our research teams to work on challenging problems that would require strong research and engineering skills to achieve a deep understanding of the problems, develop innovative approaches, and scale up with massive data and compute. Our efforts are to push the bar in the following research directions: Computer Vision, Natural Language Processing, Speech, Reinforcement Learning & Reasoning, Core Machine Learning and Creativity. We are proposing Summer, Fall, and Winter start dates. Internships will be awarded on a rolling basis and candidates are encouraged to apply early.To learn more about our research, visit https://research.facebook.com.

Research Scientist Intern,AI Core Machine Learning (PhD) 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, etc.
• Draft research publications.
• Present research outcomes to internal and external audiences.

Minimum Qualifications:

• Currently has, or is in the process of obtaining a PhD 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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