Senior Machine Learning Engineer, TikTok Core Feed Recommendation - User Growth

Tiktok - San Jose, CA

Full-time - Mid Level

San Jose, CA

Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services

About the position

The User Growth team at TikTok is a vital part of the Core Feed Recommendation team, focusing on enhancing user acquisition and retention strategies through advanced machine learning and recommendation algorithms. This role involves collaborating with cross-functional teams to analyze user growth challenges and implement effective solutions, ultimately driving the growth of TikTok's global user base.

Responsibilities

• Implement machine learning, recommendation, and causal inference algorithms at large scales to optimize and improve new user acquisition efficiency and user retention across the whole user life cycle.
• Work cross-functionally with product managers, data scientists, and product engineers to understand insights, formulate problems, design and refine machine learning algorithms, and further drive the exciting growth of global TikTok users.
• Analyze and pinpoint specific issues affecting user growth across various regions and markets, and utilize algorithmic levers for targeted optimization.
• Run regular A/B tests, perform analysis, and iterate algorithms accordingly.
• Have a good understanding of end-to-end machine learning systems. Work with infra teams on improving efficiency and stability.

Requirements

• Strong programming skills in Python and/or C/C+, and a deep understanding of data structures and algorithms.
• Familiar with the architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/Pytorch/MXNet).
• Possess keen data insights and clear organization skills, capable of addressing various problems and challenges in growth scenarios.
• Good communication and teamwork skills, be passionate about learning new techniques and taking on challenging problems.

Nice-to-haves

• Minimum of 3 years of experience in one or more of the areas: recommender systems, machine learning, deep learning, pattern recognition, data mining, computer vision, NLP, content understanding or multimodal machine learning.
• Prior industry experience with the main components of recommendation systems (retrieval, ranking, re-ranking, cold-start, etc.) is a plus but not required.
• Publications at main conferences such as KDD, NeurIPS, WWW, SIGIR, WSDM, CIKM, ICLR, ICML, IJCAI, AAAI, RecSys or related conferences.
• Strong tracking record of success in data mining, machine learning, or ACM-ICPC/NOI/IOI competitions.
• Participation in public/open-source AI-related projects which are of high visibility.

Benefits

• Inclusive workplace culture
• Commitment to diversity and inclusion
• Reasonable accommodations for candidates with disabilities or other protected reasons