Research Engineer - TikTok Ads Core ML, Ranking
San Jose, CA (On-site)
Posted 1 day ago
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Job Summary
Own and advance a global advertising delivery and ranking system by driving ML/RL/LLM research, building scalable frameworks, and collaborating with product teams to optimize the entire funnel and deliver better returns.
- Lead end-to-end ML-driven ranking and delivery optimization
- Collaborate with product teams across scenarios to scale globally
- Develop frontier technologies for efficient, ROI-focused ads delivery
Job Description
Responsibilities
- 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
- 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
- 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.
Benefits
- Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
TikTok is the world's leading destination for short-form video. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible. Our employees lead with curiosity, and move at the speed of culture. Combined with our company's flat structure, you'll be given dynamic opportunities to make a real impact on a rapidly expanding company as you grow your career. We have offices across Asia Pacific, the Middle East, Europe, and the Americas – and we're just getting started.
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