Machine Learning Engineer, TikTok Brand Ads

Description

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 optimize the entire advertising ranking funnel—including retrieval (candidate generation), coarse-ranking, and fine-ranking models.
- Apply state-of-the-art deep learning and recommendation algorithms to improve ad relevance and performance.
- Implement an efficient large-scale video content indexing and retrieval system to support vector-level matching and filtering between ad semantics and millions of native videos.
- Set up content understanding pipelines for various business scenarios, processing tens of millions of videos daily.
- Apply technologies such as Embedding Distillation and Hard Negative Mining to optimize the training process.

Requirements

Minimum Qualifications:
- BS degree in Computer Science, Computer Engineering or other relevant majors.
- Excellent programming, debugging, and optimization skills in one or more general purpose programming languages including but not limited to: Go, C/C++, Python.
- Ability to think critically and to formulate solutions to problems in a clear and concise way.
- Relevant professional experience with machine learning, data mining, data analysis, distribution system.
- Experience with one or more of the following: Machine Learning, Deep Learning, NLP, ranking systems, recommendation systems, backend, large-scale systems, data science, full-stack.
- Good product sense and experience designing and implementing product features.

Preferred Qualifications:
- Possess strong technical background/experience in search/advertising/recommendation systems, NLP, LLM, etc.
- Good understanding in one of the following domains: brand ads, content ads, auction, bidding, ranking, and ads forecasting.
- A strong passion for tackling complex modeling challenges and building large-scale recommendation systems.
- Experience in video understanding-related projects.