Algorithm Engineer - Global Live - Recommendation Strategy (Singapore)

The strategy team's mission is to use model-driven and data-driven ways to promote core indicators on LIVE revenue and user engagement, which includes: 1. Build prediction/uplift NN model to improve LIVE feature intelligence; 2. Mine and interfere potential crowds to gain a higher ROI operation for both LIVE streamer and watcher; 3. Make detailed analysis on user data and system data to find out user experience optimiaztion zoom; 4. Other work like user growth strategy, cold-start strategy, etc.

Job Description 1. Participate in the optimization of TikTok LIVE monetization business and serve the LIVE experience of billions of global users; 2. Adopt cutting-edge machine learning model to scientifically model massive user behaviour and consumption data, build high-performance/high-reliability architecture services, and bring users the ultimate experience in LIVE consumption; 3. Deeply participate in product/business discussions, fully analyze and understand user behaviour patterns, and provide optimization directions for the overall business.

Minimum Qualifications 1. Familiar with C/C++/ Python development, familiar with Linux multi-threaded /multi-process; 2. Familiar with commonly used machine learning and deep learning algorithms, including but not limited to boost machine, dnn, reinforcement learning, etc.; 3. Solid maths foundation, including probability statistics, numerical optimization, etc., sensitive to data, good at discovering, analyzing and solving problems from data; 4. Familiar with at least one of Flink, Spark, Hive etc., with big data parallel processing experience;

Preferred Qualifications 1. Experience in large-scale recommend system, advertising system, search engine is preferred; 2. Experience in game value strategy, e-commerce value strategy, and user psychology research is preferred;