About this role
Full Time Machine Learning Engineer, Commerce Ads Ranking in AI at TikTok in San Jose, California, United States. Apply directly through the link below.
At a glance
- Work mode
- Office
- Employment
- Full Time
- Location
- San Jose, California, United States
Core stack
- Optimization
- Performance
- Distributed
- Efficiency
- Feedback
- Design
- LLM
Quick answers
What skills are required?
Optimization, Performance, Distributed, Efficiency, Feedback, Design, LLM.
TikTok is hiring for this role. Visit career page
San Jose, United States
TikTok Commerce Ads Ranking team is dedicated to model optimization in the ads delivery system. Our goal is to improve monetization efficiency of TikTok Commerce Ads products through model optimization in full funnel. Besides, our team aims to design and implement universal modeling solutions, and solve the long-standing problems of ranking algorithms.
At TikTok Commerce Ads Ranking team, you can optimize model in recall / rough sort / fine sort, and explore innovative algorithms to break through the ceiling of ads performance.
Responsibilities：
1. Optimizing model in ads delivery system: feature engineering, model structure, auto crossing, ads cold start, modeling delayed feedback, multi-task learning, sequence modeling
2. Algorithm and system co-design: retrieval algorithm, sample mining, long sequence
3. Exploring large-scale distributed training framework: GPU tuning, feature processing, synchronous training
Improving ads delivery efficiency in privacy-preserving environments
4. LRM, the next generation rec system using LLM learning paradigm: entity understanding, end2end Generative Recommendation, sequence only based Recommendation, Mixture of Export
At TikTok Commerce Ads Ranking team, you can optimize model in recall / rough sort / fine sort, and explore innovative algorithms to break through the ceiling of ads performance.
Responsibilities：
1. Optimizing model in ads delivery system: feature engineering, model structure, auto crossing, ads cold start, modeling delayed feedback, multi-task learning, sequence modeling
2. Algorithm and system co-design: retrieval algorithm, sample mining, long sequence
3. Exploring large-scale distributed training framework: GPU tuning, feature processing, synchronous training
Improving ads delivery efficiency in privacy-preserving environments
4. LRM, the next generation rec system using LLM learning paradigm: entity understanding, end2end Generative Recommendation, sequence only based Recommendation, Mixture of Export