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🚀 Internship

Machine Learning Engineer Intern (Foundation Model Applications) - 2024 Off-Cycle (PHD)

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TikTok

1mo ago

🚀 Off-cycle Internship

San Jose

AI generated summary

  • The ideal candidate for the Machine Learning Engineer Intern (Foundation Model Applications) position at Tiktok must be a PHD student graduating December 2024 onwards, with hands-on experience in areas such as Machine Learning, Deep Learning, Recommender Systems, Data Mining, Natural Language Processing, or Computer Vision. They should have strong programming skills in Python and/or C/C++, familiarity with mainstream machine learning frameworks, excellent communication, teamwork skills, and a passion for learning new techniques. Additionally, they should have the ability to obtain and maintain work authorization. Preferred qualifications include prior research/industry experience, publications at major AI-related conferences, a strong track record in AI-related competitions, participation in open-source AI projects, and a passion for First Principles Thinking.
  • The Machine Learning Engineer Intern at Tiktok will be responsible for optimizing recommendation systems, deploying and fine-tuning foundation models, collaborating with cross-functional teams, running A/B tests, and improving the efficiency of machine learning systems.

Off-cycle Internship

DataSan Jose

Description

  • Internships at TikTok aim to provide students with hands-on experience in developing fundamental skills and exploring potential career paths. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. This Internship Program runs for 12 weeks beginning in May/June 2024

Requirements

  • Currently pursuing PHD degree
  • Graduating December 2024 onwards with the intent to return to degree program after the completion of the internship
  • Able to commit to working for 12 weeks during Summer 2024
  • Hands-on experience in one or more of the following areas: Machine Learning, Deep Learning, Recommender Systems, Data Mining, Natural Language Processing, or Computer Vision
  • Strong programming skills in Python and/or C/C++, and a deep understanding of data structures and algorithms
  • Familiar with architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/PyTorch/MXNet)
  • Excellent communication and teamwork skills, and a passion for learning new techniques and tackling challenging problems.
  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
  • Preferred Qualifications:
  • Prior research/industry experience with deploying, prompting, and fine-tuning foundation models
  • Prior research/industry experience with applied machine learning, or large-scale recommendation systems
  • Publications at major AI-related conferences such as NeurIPS, ICML, ICLR, AAAI, IJCAI, ACL, NAACL, EMNLP, CVPR, ICCV, ECCV, KDD, ICDM, SDM, RecSys, or simply on arXiv but with large impact
  • Strong track record in AI-related competitions, or participation in public/open-source AI-related projects of high visibility
  • A strong passion for First Principles Thinking

Education requirements

Currently Studying
PhD

Area of Responsibilities

Data

Responsibilities

  • Understand and optimize TikTok's industry-leading recommendation system
  • Deploy, prompt, and fine-tune cutting-edge foundation models
  • Apply foundation models to enhance TikTok's recommendation system and product offerings, improving the experience of billion-scale consumers and creators
  • Collaborate with cross-functional teams, including product managers, data scientists, and product engineers, to form and solve problems, refine machine learning algorithms, and communicate results
  • Regularly run A/B tests, perform analyses, and iterate algorithms based on results
  • Work with infrastructure teams on improving the efficiency and stability of machine learning systems

Details

Work type

Full time

Work mode

office

Location

San Jose