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

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy.

TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join UsCreation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.

Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day. To us, every challenge, no matter how difficult, is an opportunity;

to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always. At TikTok, we create together and grow together.

That's how we drive impact - for ourselves, our company, and the communities we serve. Join us. TikTok Core Feed Recommendation team sits in the center of TikTok, and designs, implements, and improves the core recommendation algorithm that powers the "for you" feed, "following" feed, etc.

of the TikTok app. The recommendation system we built connects hundreds of millions of users with relevant content out of billions of videos in real-time, and inspires high-quality content creation for millions of creators on the platform.

The User Growth team is an essential pillar of the Core Feed Recommendation team, directly responsible for implementing and refining new user acquisition and retention strategies.

Our team is committed to achieving TikTok's ultimate goals through developing high-performance models and sound strategies.

We take pride in our rigorous approach to applied research, innovative system design, and steadfast pragmatism. We are looking for strong research scientists and engineers at all levels, who are excited about growing their business understanding, building highly scalable and reliable software, and partnering across disciplines with global teams, in pursuit of excellence.

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.

Minimum Qualifications- 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.

Preferred Qualifications- 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, 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.
30+ days ago