Research Engineer - TikTok Ads Core ML, Ranking

Description

TikTok Ads Core ML Team aims at creating automatic delivery products for the next generation and developing advertising as a global business, instead of just a monetization tool to consolidate the delivery funnel framework allowing multiple teams to iterate parallel.

We're looking for innovative Research Engineers focused on ML/RL/LLM or other relevant professional domains, to join our TikTok Ads Core ML Ranking team. Ads Core Ranking team specifically focuses on maximizing delivery system efficiency and revenue growth through state-of-the-art models and frameworks. Our research topics include but not limited to: Generative Retrieval and Large Recommendation Model, LLM-based Ranking Application, and Optimization of System Resource Allocation with ROI target.

As part of our team, you will be responsible for:
- Optimize efficiency across the entire advertising funnel, including Recall&Rough-sort, Fine-sort(CTR/CVR), format/creative personalization and system resource allocation.
- Research & develop a global advanced advertising delivery system through frontier technologies, including ML/DL, RL, LLM and also scaling law in ads recommendation.
- Design & Set up system framework and standard to continuously improve overall efficiency and meet different vertical business needs.
- Work with product and business teams from various scenarios with global impact.

All of our team effort, is to continuously pursue and establish a world-leading ranking model & framework that always benefits our collaborators, users and customers to get better returns.

Requirements

Minimum Qualifications:
- BS/MS degree in Computer Science, Statistics, Operation Research, Applied Mathematics, Physics or similar quantitative fields/with related experience.
- Research/industry experience in one or more of the following areas: machine learning, deep learning, statistical models and applied mathematical methods etc.
- Solid programming skills, proficient in C/C++ and Python. Familiar with basic data structure and algorithms.
- Familiar with at least one mainstream machine learning programming framework (TensorFlow/PyTorch/MXNet).
- Familiarity with online experimentation and analytics. Familiarity with big data systems including Hadoop and Spark.
- Curiosity towards learning and applying new technologies.

Preferred Qualifications:
- Participation in national math/coding competitions (ACM/Hacker Cup/Hash Code/USACO/IOI/CCPC etc.)
- Paper publications/citations in NLP/CV/Recommender System(RecSys/KDD/ICML/CVPR/NeurIPs, etc.)
- Experience in LLM, reinforcement learning, transfer learning, and counter-factual optimization is a plus.