About this role
Full Time Machine Learning Engineer - Search Ads in machine learning 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
- Natural Language Processing
- Machine Learning
- Data Analysis
- Optimization
- Architecture
- Distributed
- Innovation
- NLP
Quick answers
What skills are required?
Natural Language Processing, Machine Learning, Data Analysis, Optimization, Architecture, Distributed, Innovation, NLP.
TikTok is hiring for this role. Visit career page
San Jose, United States
The Search Ads team constantly pushes the boundaries of general search engine monetization across our apps, including TikTok, TopBuzz, BuzzVideo, and more, building a globally leading Search Ads monetization system. At the Search Ads team, you will have the chance to work on large-scale distributed storage and architecture, NLP, Rank, and IR related problems. You will be also deeply involved in the innovation and optimization of our Ad format, creative display, and the ROI of ads delivery. We are looking for candidates who brave difficulties, share a passion for tackling complexity and developing our Search Ads product from 0 to 1 with a world-class team of passionate engineers.
What You'll Do:
• Participate in the development of a large-scale Ads system
• Responsible for relevance model and strategy optimization, such as semantic matching models, active learning, text/photo/video multi-model, ranking strategy, etc
• Participate in the development and iteration of Ads algorithms by using Machine Learning.
• Work on NLP (Natural Language Processing) capability improvement and query understanding, such as query classification, seq2seq, NER (Named Entity Recognition), knowledge graph, bidword optimization, etc
• Work on CTR/CVR model estimation accuracy, data analysis, modeling, feature engineering
• Research and develop Ads pacing algorithms, ads traffic control, etc
• Partner with product managers and product strategy & operation team to define product strategy and features
What You'll Do:
• Participate in the development of a large-scale Ads system
• Responsible for relevance model and strategy optimization, such as semantic matching models, active learning, text/photo/video multi-model, ranking strategy, etc
• Participate in the development and iteration of Ads algorithms by using Machine Learning.
• Work on NLP (Natural Language Processing) capability improvement and query understanding, such as query classification, seq2seq, NER (Named Entity Recognition), knowledge graph, bidword optimization, etc
• Work on CTR/CVR model estimation accuracy, data analysis, modeling, feature engineering
• Research and develop Ads pacing algorithms, ads traffic control, etc
• Partner with product managers and product strategy & operation team to define product strategy and features