Responsibilities
About the Team
Recommendation algorithm team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is dynamic, fast-pacing, collaborative and impact-driven.
Responsibilities - What You'II Do
- Participate in the recommendation algorithm of the live streaming business and make contributions to a world-class large scale recommendation system
- Optimize the core algorithms and strategies (recall, coarse ranking, fine ranking, mixed ranking, diversity, etc.) through modeling technologies including deep learning, representation learning, multi-task learning, causal inference, and sequence modeling. Ensure every user finds relevant creators and enjoys the fun of live streaming.
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- Continuously improve recommendation technology with deep understanding of the ecological roles of users, creators, platforms, drive growth in user experience, creator growth, platform revenue, and create a virtuous cycle of livestream ecosystem
- Work closely with the product and operations team, excel in technological innovation with deep integration of business characteristics, and help achieve long-term and short-term business development goals.
Qualifications
Minimum Qualifications
- Solid programming foundation, good programming style and work habits, solid knowledge of data structures and algorithms.
- Has strong theoretical foundation and extensive practical experience in the field of machine learning/deep learning , familiar with at least one mainstream deep learning framework.
- Has exceptional ability to analyze and solve problems, passionate about solving challenging problems; good at communication, proactive at work, has a strong sense of responsibility, and possess good teamwork skills.
- Priority will be given to individuals who have published papers at top conferences, won competitions such as ACM/machine learning, or have experience in core algorithm businesses such as large-scale recommendation systems, computational advertising, and search engines.
Job Information
[For Pay Transparency] Compensation Description (annually)
The base salary range for this position in the selected city is $126000 - $221760 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.