Machine Learning Engineer, TikTok Brand Ads
Aug 21, 2026 San Jose
About us
TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
- Size: 10,000+ employees
- Industry: Technology: Consumer Internet
Why you should apply for a job to TikTok:
4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity
Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›
Responsibilities
Build the most powerful content understanding engine on the world's leading short-video platform! Retrieve and rerank billions of videos, redefine the intelligent connection between advertising and content with multimodal large language model.
TikTok Brand Ads team is hiring multiple MLE/SWE roles, focusing on the in-depth application of multimodal large language models in brand advertising. Join us to build the most powerful content understanding engine on the world's leading short-video platform, retrieve and rerank billions of videos and redefine the intelligent connection between advertising and content with multimodal large language model!
The 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.
Key technical directions:
- Multimodal-LLM model development
- Generative retrieval & ranking technology
- Ads ranking
- LLM inference optimization
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:
- Use large video models to model the semantic meaning of video content and construct structured vector representations.
- Build multimodal representations of brand ads (video/picture + title/script + brand semantics, etc.) to achieve cross-modal alignment.
- 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.
Qualifications
Minimum Qualifications:
- BS degree in Computer Science, Computer Engineering or other relevant majors, with related work experience
- 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 NLP, LLM, search/advertising/recommendation systems, etc.
- Good understanding in one of the following domains: brand ads, content ads, auction, bidding, ranking, and ads forecasting.
- Have a strong passion for large language models.
- Publications in top-tier conferences and experience in video understanding-related projects.
Job Information
[For Pay Transparency] Compensation Description (annually)
The base salary range for this position in the selected city is $162000 - $316800 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
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).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.
Build the most powerful content understanding engine on the world's leading short-video platform! Retrieve and rerank billions of videos, redefine the intelligent connection between advertising and content with multimodal large language model.
TikTok Brand Ads team is hiring multiple MLE/SWE roles, focusing on the in-depth application of multimodal large language models in brand advertising. Join us to build the most powerful content understanding engine on the world's leading short-video platform, retrieve and rerank billions of videos and redefine the intelligent connection between advertising and content with multimodal large language model!
The 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.
Key technical directions:
- Multimodal-LLM model development
- Generative retrieval & ranking technology
- Ads ranking
- LLM inference optimization
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:
- Use large video models to model the semantic meaning of video content and construct structured vector representations.
- Build multimodal representations of brand ads (video/picture + title/script + brand semantics, etc.) to achieve cross-modal alignment.
- 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.
Minimum Qualifications:
- BS degree in Computer Science, Computer Engineering or other relevant majors, with related work experience
- 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 NLP, LLM, search/advertising/recommendation systems, etc.
- Good understanding in one of the following domains: brand ads, content ads, auction, bidding, ranking, and ads forecasting.
- Have a strong passion for large language models.
- Publications in top-tier conferences and experience in video understanding-related projects.
Build the most powerful content understanding engine on the world's leading short-video platform! Retrieve and rerank billions of videos, redefine the intelligent connection between advertising and content with multimodal large language model.
TikTok Brand Ads team is hiring multiple MLE/SWE roles, focusing on the in-depth application of multimodal large language models in brand advertising. Join us to build the most powerful content understanding engine on the world's leading short-video platform, retrieve and rerank billions of videos and redefine the intelligent connection between advertising and content with multimodal large language model!
The 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.
Key technical directions:
- Multimodal-LLM model development
- Generative retrieval & ranking technology
- Ads ranking
- LLM inference optimization
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:
- Use large video models to model the semantic meaning of video content and construct structured vector representations.
- Build multimodal representations of brand ads (video/picture + title/script + brand semantics, etc.) to achieve cross-modal alignment.
- 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.
Qualifications
Minimum Qualifications:
- BS degree in Computer Science, Computer Engineering or other relevant majors, with related work experience
- 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 NLP, LLM, search/advertising/recommendation systems, etc.
- Good understanding in one of the following domains: brand ads, content ads, auction, bidding, ranking, and ads forecasting.
- Have a strong passion for large language models.
- Publications in top-tier conferences and experience in video understanding-related projects.
Job Information
[For Pay Transparency] Compensation Description (annually)
The base salary range for this position in the selected city is $162000 - $316800 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
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).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.
Responsibilities
ResponsibilitiesBuild the most powerful content understanding engine on the world's leading short-video platform! Retrieve and rerank billions of videos, redefine the intelligent connection between advertising and content with multimodal large language model.
TikTok Brand Ads team is hiring multiple MLE/SWE roles, focusing on the in-depth application of multimodal large language models in brand advertising. Join us to build the most powerful content understanding engine on the world's leading short-video platform, retrieve and rerank billions of videos and redefine the intelligent connection between advertising and content with multimodal large language model!
The 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.
Key technical directions:
- Multimodal-LLM model development
- Generative retrieval & ranking technology
- Ads ranking
- LLM inference optimization
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:
- Use large video models to model the semantic meaning of video content and construct structured vector representations.
- Build multimodal representations of brand ads (video/picture + title/script + brand semantics, etc.) to achieve cross-modal alignment.
- 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.
Qualifications
QualificationsMinimum Qualifications:
- BS degree in Computer Science, Computer Engineering or other relevant majors, with related work experience
- 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 NLP, LLM, search/advertising/recommendation systems, etc.
- Good understanding in one of the following domains: brand ads, content ads, auction, bidding, ranking, and ads forecasting.
- Have a strong passion for large language models.
- Publications in top-tier conferences and experience in video understanding-related projects.
Additional Information
- Salary: $162,000 – $316,800
- Posted: Aug 21, 2026