2023/2024 TikTok Machine Learning Fairness PhD Internship

Filed in Internships by on January 10, 2023 0 Comments

The Machine Learning Fairness team in London is working on challenging problems in the field of Responsible AI, specifically focusing on Fairness, Transparency, and Explainability. Working at TikTok gives you the unique opportunity to do cutting-edge research and also offers you opportunities to deploy your model into production and affect millions of users.

Responsibilities

  • Participate in cutting-edge research on machine learning, deep learning, natural language processing, computer vision, recommendation systems, search ranking and other fields.
  • Collaborate closely with TikTok’s research team and work in the ever-growing field of machine learning fairness and responsible AI. This includes but is not limited to AI interpretability & explainability, causal inference and robustness.
  • Participate in model building, performance optimization, and project deployment.

Eligibility

  • Open to individuals currently pursuing a relevant PhD with a bachelor’s/master’s degree in computer science/mathematics/computational statistics or related subjects;
  • Available to work full time for a minimum of three months;
  • Start dates are flexible and will be discussed during the recruitment process;
  • Passion for modern machine learning and cutting-edge technology;
  • Good programming ability, proficiency in at least one programming language, such as Python, C/C++, etc. (Python preferred);
  • Familiar with commonly used machine learning algorithms and experience using at least one deep learning framework. (such as Pytorch, TensorFlow, JAX etc.);
  • Good communication skills, teamwork spirit, analytical skills and learning ability;
  • Publications in top machine learning journals or conferences (such as ICML, NeurIPS, JMLR, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, AISTATS, KDD, SIGIR, WWW etc.) are a plus.

How To Apply

Click Here To Apply

For more information, visit TikTok Internship.

Application Deadline: Ongoing

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