Machine Learning Engineer, Commerce Ads Ranking

Responsibilities

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

Qualifications

Minimum Qualifications:
1. Have excellent coding skills, be familiar with C++/Python, and have a solid foundation in data structures and algorithms.
2. Solid foundation in machine learning, familiar with common algorithm models such as FM, LR, GBDT, DNN, Wide&Deep, etc.
3. Excellent learning ability and good teamwork spirit. And excellent analytical and problem-solving skills.
Papers in conferences such as KDD, NeurIPS, SIGIR etc, or experience in data mining/machine learning related competitions are a good fit.
4. Bachelor degree or above in computer science or related majors, work experience in advertising/recommendation/search ranking are preferred.
Preferred Qualifications:
1. Papers in conferences such as KDD, NeurIPS, SIGIR etc, or experience in data mining/machine learning related competitions are a good fit.
2. Bachelor degree or above in computer science or related majors, work experience in advertising/recommendation/search ranking are preferred.

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

Responsibilities

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

Qualifications

Qualifications

Minimum Qualifications:
1. Have excellent coding skills, be familiar with C++/Python, and have a solid foundation in data structures and algorithms.
2. Solid foundation in machine learning, familiar with common algorithm models such as FM, LR, GBDT, DNN, Wide&Deep, etc.
3. Excellent learning ability and good teamwork spirit. And excellent analytical and problem-solving skills.
Papers in conferences such as KDD, NeurIPS, SIGIR etc, or experience in data mining/machine learning related competitions are a good fit.
4. Bachelor degree or above in computer science or related majors, work experience in advertising/recommendation/search ranking are preferred.
Preferred Qualifications:
1. Papers in conferences such as KDD, NeurIPS, SIGIR etc, or experience in data mining/machine learning related competitions are a good fit.
2. Bachelor degree or above in computer science or related majors, work experience in advertising/recommendation/search ranking are preferred.