Machine Learning Engineer, TikTok Brand Ads

TikTok Inc.; San Jose (CA); On-site; USD 162,000 - 317,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) match

Paid parental leave

Wellbeing benefits

Paid holidays

Paid sick days

Personal time off

Job summary

TikTok Inc. is hiring for an engineering role focused on building and optimizing the complete advertising ranking pipeline.

You will develop retrieval, coarse and fine ranking models and apply cutting-edge deep learning and recommendation techniques to improve ad relevance across a vast video platform. The role involves designing scalable back-end systems, indexing millions of videos, and deploying robust pipelines to process large-scale data daily.

Qualifications

• BS degree in Computer Science, Computer Engineering or a related field.
• Proficient in programming languages including Go, C/C++, Python.
• Experience with machine learning, data mining, data analysis and distribution systems.
• Strong problem solving, critical thinking and clear communication.
• Experience with ML, DL, NLP, ranking/recommendation systems, backend and large-scale systems.
• Good product sense and ability to design and implement features.

Responsibilities

• Develop and optimize the advertising ranking funnel from retrieval to fine-ranking models.
• Apply state-of-the-art DL and recommendation algorithms to improve ad relevance.
• Build large-scale video content indexing and retrieval to support vector-level matching across millions of videos.
• Set up content understanding pipelines for multiple business scenarios processing tens of millions of videos daily.
• Apply Embedding Distillation and Hard Negative Mining to optimize training.

Skills

Go

C/C++

Python

Machine Learning

Data Mining

Data Analysis

Distributed Systems

Back-end Systems

Education

BS in Computer Science / Computer Engineering

Job description

Responsibilities

TikTok Brand Ads 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.

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:

• Develop and optimize the entire advertising ranking funnel-including retrieval (candidate generation), coarse-ranking, and fine-ranking models.
• Apply state-of-the-art deep learning and recommendation algorithms to improve ad relevance and performance.
• 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.
• 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 search/advertising/recommendation systems, NLP, LLM, etc.
• Good understanding in one of the following domains: brand ads, content ads, auction, bidding, ranking, and ads forecasting.
• A strong passion for tackling complex modeling challenges and building large-scale recommendation systems.
• 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.