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Machine Learning Engineer, Search Recommendation

$122.57k - $316.8k

TikTok

The Machine Learning Engineer role in Search Recommendation focuses on building and improving search recommendation algorithms and models to strengthen TikTok and TikTok Mall search traffic and users search understanding.

Role Summary

You will develop search recommendation services and models for TikTok to drive increased search traffic and improve how users understand and express search intent across the platform and TikTok Mall.

Key Responsibilities

  • Enhance search recommendation services and models for TikTok, aiming to increase search traffic on TikTok and TikTok Mall while improving user search understanding.
  • Optimize recommender systems using hyperscale machine learning models, spanning recall and first-stage ranking through final-stage ranking within an end-to-end workflow.
  • Investigate the upper limits of short text recommendation and broader recommendation technology, with attention to the interaction between recommendation and NLP technologies.

Required Qualifications

  • 3+ years of work experience in machine learning.
  • Good product sense with a focus on user experience.
  • Bachelor or advanced degree in computer science or a related technical discipline.
  • Excellent coding skills with solid knowledge of data structures and algorithms.
  • Strong analysis, modeling, and problem-solving ability, with capacity to identify the core of problems from complex data.

Technologies and Focus Areas

  • Machine learning
  • NLP
  • CV
  • Recommendation
  • Multi-modal technology
  • Hyperscale machine learning models

Compensation

  • Salary range: USD 122,574 - 316,800 per year .
  • Compensation may vary outside this range based on factors including qualifications, skills, competencies and experience, and location.
  • Base pay is part of the Total Package and the 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.
  • The company reserves the right to modify or change benefits programs at any time, with or without notice.

Benefits

  • Day one access to medical, dental, and vision insurance
  • 401(k) savings plan with company match
  • Paid parental leave
  • Short-term and long-term disability coverage
  • Life insurance
  • Wellbeing benefits
  • 10 paid holidays per year
  • 10 paid sick days per year
  • 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)
  • Additional discretionary bonuses/incentives, and restricted stock units (may be eligible)

About the Team

The Search Growth team leads the development of the search recommendation algorithm for TikToks rapidly expanding global e-commerce enterprise. The team uses advanced machine learning technology including NLP, CV, recommendation, and multi-modal technology to build a search recommendation engine intended to support the ultimate e-commerce search experience for over 1 billion active TikTok users worldwide. The teams mission is to create a world where there are no hard-to-sell, overpriced products.

Location and Work Type

  • San Jose, CA (onsite)

Los Angeles County (Unincorporated) Fair Chance Notice

  • 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.
  • The company believes that criminal history may have a direct, adverse and negative relationship on job duties, potentially resulting in withdrawal of a conditional offer of employment.
  • Examples of duties referenced include interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues; appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and exercising sound judgment.
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Vacancy posted 6 hours ago
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