With the enormous growth of live streaming in recent years, our mission is to inspire real-time interactions and help live streamers connect to people from all corners of the globe. We are looking for passionate and talented engineers to join us to build and optimize a real-time, high-performance, large-scale distributed infrastructure for live streaming in TikTok. You will be deeply involved in the developmental lifecycle of critical product features and collaborate closely with product managers to deliver the best live streaming experience for all live streamers and audience alike.
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:
- Build prediction/uplift NN model to improve LIVE feature intelligence;
- Mine and interfere potential crowds to gain a higher ROI operation for both LIVE streamer and watcher;
- Make detailed analysis on user data and system data to find out user experience optimiaztion zoom;
- Other work like user growth strategy, cold-start strategy, etc.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
Job Description
- Participate in the optimization of TikTok LIVE monetization business and serve the LIVE experience of billions of global users;
- 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;
- Deeply participate in product/business discussions, fully analyze and understand user behaviour patterns, and provide optimization directions for the overall business.
Minimum Qualifications
- Currently pursuing a PhD in Computer Science or a related engineer discipline.
- Familiar with C/C++/ Python development, familiar with Linux multi-threaded /multi-process;
- Familiar with commonly used machine learning and deep learning algorithms, including but not limited to boost machine, dnn, reinforcement learning, etc.;
- Solid maths foundation, including probability statistics, numerical optimization, etc., sensitive to data, good at discovering, analyzing and solving problems from data;
- Familiar with at least one of Flink, Spark, Hive etc., with big data parallel processing experience;
Preferred Qualification
- Experience in large-scale recommend/advertising systems, risk control systems or LLM is preferred