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Machine Learning Engineer, TikTok Video Recommendation-Location Products

AT TikTok
TikTok

Machine Learning Engineer, TikTok Video Recommendation-Location Products

San Jose, CA

Responsibilities

Team introduction:
TikTok Video Recommendation Team is responsible for the personalized recommendation algorithms for TikTok's hundreds of millions of global users. Here, you will collaborate with top algorithm engineers in the industry, leveraging your expertise in deep learning, recommendation algorithms, and large models to continuously transform and enhance the TikTok user experience and content ecosystem.

In particular, the local service recommendation team focuses on targeting user experience and transaction scale optimization for lifestyle service content, including hotels, travel, dining, and more. This role aims to pioneer new content and revenue streams for the company.

Responsibility:

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1. Responsible for developing content recommendation algorithms for international local services. Collaborate with the team to build an industry-leading recommendation system, providing high-value content for hundreds of millions of global users, promoting business growth and enhancing user experience.
2. Delve deeply into the influence value of content in local services, leading to growth and ecological optimization of content in vertical fields. Based on a profound understanding of user scenarios and content preferences, combined with product and operational means, achieve scalable growth of content, and verify the value contribution of vertical content to the overall business.
3. Master the core algorithms of the recommendation system, focus on multimodal data such as video, live, geographic location, and goods, optimize recall strategies, recommendation models and multi-objective optimization mechanisms, fully apply machine learning, deep learning and LLM algorithms to improve system performance and recommendation effects.
4. Focus on user behavior and experience optimization, deeply analyze user behavior, optimize user experience in the process of creation and browsing through data mining and other technical skills, stimulate the enthusiasm of creators, and improve user stickiness and satisfaction.

Qualifications

Minimum Qualifications:
- Master's degree or above in computer science or a related field.
- Strong programming skills with a solid foundation in machine learning/deep learning
- Passionate about recommendation algorithms and machine learning, with a willingness to learn, think critically, delve deeply, and innovate.
- Excellent problem analysis and solving skills, along with strong communication abilities and a collaborative team spirit.

Preferred Qualification:
- Experience in recommendation systems, computational advertising, search engines, or LLM-related fields are preferred

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $145000 - $250000 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.

Client-provided location(s): San Jose, CA, USA
Job ID: TikTok-7483363896075749640
Employment Type: Other

Perks and Benefits

  • Health and Wellness

    • Health Insurance
    • Dental Insurance
    • Vision Insurance
    • HSA
    • Life Insurance
    • Fitness Subsidies
    • Short-Term Disability
    • Long-Term Disability
    • On-Site Gym
    • Mental Health Benefits
    • Virtual Fitness Classes
  • Parental Benefits

    • Fertility Benefits
    • Adoption Assistance Program
    • Family Support Resources
  • Work Flexibility

    • Flexible Work Hours
    • Hybrid Work Opportunities
  • Office Life and Perks

    • Casual Dress
    • Snacks
    • Pet-friendly Office
    • Happy Hours
    • Some Meals Provided
    • Company Outings
    • On-Site Cafeteria
    • Holiday Events
  • Vacation and Time Off

    • Paid Vacation
    • Paid Holidays
    • Personal/Sick Days
    • Leave of Absence
  • Financial and Retirement

    • 401(K) With Company Matching
    • Performance Bonus
    • Company Equity
  • Professional Development

    • Promote From Within
    • Access to Online Courses
    • Leadership Training Program
    • Associate or Rotational Training Program
    • Mentor Program
  • Diversity and Inclusion

    • Diversity, Equity, and Inclusion Program
    • Employee Resource Groups (ERG)

Company Videos

Hear directly from employees about what it is like to work at TikTok.