Research Scientist Intern, Computer Vision (PhD / Masters)

Meta is seeking Research Interns to join its AI Research (FAIR) team. We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning, reinforcement learning, computational statistics, and applied mathematics. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale. Many of our interns publish at top tier conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, and ACL. We are proposing Summer, Fall, and Winter start dates. To learn more about our research, visit https://research.facebook.com.

Research Scientist Intern, Computer Vision (PhD / Masters) Responsibilities:

• Perform research to advance the science and technology of intelligent machines
• Perform research that enables learning the semantics of data (3D geometries, mocap, images, video, text, audio, and other modalities)
• Devise better data-driven models of human behavior, and/or algorithms that help users to create 2D/3D graphical contents easily
• Contribute research that can be applied to Facebook product development

Minimum Qualifications:

• Currently has, or is in the process of obtaining, a PhD or Masters in Computer Science, Artificial Intelligence, or related fields.
• Experience in C++ or Python or other related programming language.
• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment.

Preferred Qualifications:

• Knowledge of practical optimization of neural nets, preferably using the Pytorch API.
• Publications or experience in machine learning, AI, computer vision, computer graphics, optimization, computer science, statistics, applied mathematics, or data science.
• Experience solving analytical problems using quantitative approaches.
• Ability to manipulate and analyze complex, large scale, high-dimensionality data from varying sources.
• Ability to quickly set up experiments and analyze their results.
• Experience in utilizing theoretical and empirical research to solve problems.
• Experience building systems based on machine learning and/or deep learning methods.
• Experience working and communicating cross functionally in a team environment.
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, CoRL, and SIGGRAPH.
• Intent to return to degree-program after the completion of the internship.

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