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

TikTok is the leading destination for short-form mobile video, and our mission is to inspire creativity and bring joy. The Core Feed Recommendation team is at the heart of TikTok, responsible for designing, implementing, and improving the core recommendation algorithms that power the "for you" and "following" feeds of the TikTok app. This recommendation system connects hundreds of millions of users with relevant content from billions of videos in real-time, fostering high-quality content creation for millions of creators on the platform. The User Growth team, a vital part of the Core Feed Recommendation team, focuses on implementing and refining strategies for new user acquisition and retention. We are dedicated to achieving TikTok's overarching goals by developing high-performance models and sound strategies. Our approach is characterized by rigorous applied research, innovative system design, and a commitment to pragmatism. We are seeking strong research scientists and engineers at all levels who are eager to enhance their business understanding, build scalable and reliable software, and collaborate across disciplines with global teams in pursuit of excellence. In this role, you will implement machine learning, recommendation, and causal inference algorithms at scale to optimize new user acquisition efficiency and user retention throughout the user lifecycle. You will work cross-functionally with product managers, data scientists, and product engineers to derive insights, formulate problems, design and refine machine learning algorithms, and drive the exciting growth of TikTok's global user base. Additionally, you will analyze specific issues affecting user growth across various regions and markets, utilizing algorithmic levers for targeted optimization, and run regular A/B tests to iterate algorithms based on performance analysis.

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 and work with infrastructure 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.
• Familiarity 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, with a passion for learning new techniques and tackling 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 track 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 that values diverse voices and perspectives.
• Commitment to providing reasonable accommodations in recruitment processes for candidates with disabilities, pregnancy, or sincerely held religious beliefs.