Advertising, Algorithms, Apache Hadoop, Apache Spark, Bidding, Brand Strategy, Campaigns, Computer Programming, Computer Science, Concurrency, Conferences, Construction, Content Ranking, Data Analysis, Data Modeling, Data Processing, Data Sets, Data Structures, Deep Learning, Distributed Computing, High Availability, Machine Learning, Modeling Languages, Problem Solving Skills, Product Design, Programming Languages, Publications, Systems Administration/Management
TikTok Brand Ads team is responsible for the complete technical chain from data construction, model training, offline evaluation, online deployment, inference optimization to new model exploration, covering key tasks such as multimodal semantic understanding, content matching and ranking, and cross-modal alignment.
We are looking for passionate engineers that have strong problem solving skills and algorithm understanding to build and manage systems with high performance, scalability, and availability. You will have the opportunity to partner closely with a globalized engineering and product teams in a high-impact and fast-paced environment.
What you'll do:
- Develop and maintain a highly available ad delivery system capable of supporting high-concurrency requests, fulfilling advertisers' campaign needs across brand bidding products including audience reach, video playback, and content-driven interest cultivation.
- Optimize bidding strategies and traffic allocation strategies for brand bidding advertisements.
- Leverage deep learning, large language models (LLMs), and multimodal model technologies to optimize the recall, coarse-ranking, and fine-ranking model algorithms for brand bidding ads.
- Collaborate with the product team to design and develop industry-leading brand advertising products. Minimum Qualifications(s):
- Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
- Solid foundation in data structures and algorithms, with excellent programming skills and proficiency in mainstream programming languages.
- Preferred experience in large-scale data processing and distributed computing, with familiarity in Hadoop and Spark frameworks.
- Background in traditional machine learning and deep learning. Preferred: publications in top-tier conferences such as KDD, AAAI, CIKM, WWW, RecSys, NeurIPS, ACL, or EMNLP; familiarity with the architecture and principles of TensorFlow/PyTorch/Caffe/MXNet frameworks.
- Proactive work attitude with a strong sense of ownership and excellent team collaboration spirit; willingness to take on challenging tasks.
- Strong logical thinking skills with a focus on data analysis; ability to discover valuable patterns from massive datasets.
- Strong business-oriented mindset, with technical optimization driven by and in service of achieving business objectives.