About the team
Our team works on large-scale recommendation systems for various offerings under ByteDance and its affiliates, focusing on developing recommendation algorithms/models/strategies. We are committed to developing cutting-edge solutions for e-commerce recommendation systems.
Responsibilities
- Work on recommendation systems, involving contents of various forms ranging from products, short videos to live streams, with each unified recommendation model fulfilling heterogeneous E-commerce scenarios/goals across multiple countries.
- Optimize e-commerce recommendation models at massive scales, using deep learning/transfer learning/multi-task learning techniques.
- Data mining and analysis to improve the quality of recommended contents.
- Conduct research on various topics, which aim to optimize content recommendation circulation, ranging from ensuring diversity and new discovery in recommendation contents, to cold-start problem for new users/items and discovery of high-quality products/live streamers.
- Develop innovative and state-of-the-art e-commerce models and algorithms
- Support the production of scalable and optimised AI/machine learning (ML) models
- Focus on building algorithms for the extraction, transformation and loading of large volumes of realtime, unstructured data to deploy AI/ML solutions from theoretical data science models
- Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process
- Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm.
- Work with the relevant software platforms in which the models are deployed
Minimum Qualifications
- Bachelor's degree in Computer Science or related fields.
- Strong in data structures and algorithms, with excellent problem-solving ability and programming skills
- Experience in applied machine learning, familiar with one or more of the algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks etc.
- Experience in working with main components of recommendation systems(recall, sort, reranking, cold-start problem), with good understanding of mainstream recommendation models used in the industry
- Experience in C++ and Python; at least one of the Big Data tools (For eg. Hive sql/Spark/Mapreduce; at least one of the Deep Learning tools(For eg. Tensorflow/Pytorch)
- Possess strong communication skills, positive mindset, good teamwork skills, and eagerness to learn/implement new technology and experiment
Preferred Qualifications
- Experience in personalized recommendation, online advertising, information retrieval or related fields.
- Publications at KDD、NeurIPS、WWW、SIGIR、WSDM、CIKM、ICLR、ICML、IJCAI、AAAI、RecSys and related conferences
- Excellent performance in data mining, machine learning, or ACM-ICPC/NOI/IOI competitions
- Developed widely-recognized machine learning project(s) on github or personal webpage
