Machine Learning Engineer - Search Ads

Responsibilities

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

Qualifications

Minimum Qualifications

• BS degree in Computer Science, Computer Engineering or other relevant majors.


• Excellent programming, debugging, and optimization skills in general purpose programming languages


• Ability to think critically and to formulate solutions to problems in a clear and concise way.

Preferred Qualifications

• Experience with one or more general purpose programming languages including but not limited to: Go, C/C++, Python.


• Good understanding in one of the following domains: ad fraud detection, risk control, quality control, adversarial engineering, and online advertising systems.


• Good knowledge in one of the following areas: machine learning, deep learning, backend, large-scale systems, data science, full-stack.

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.