Machine Learning Engineer for Recommendation Systems

6 days ago


San Jose, California, United States Tik Tok Full time
Job Description

We are seeking a highly skilled Machine Learning Engineer to join our team in Los Angeles or Singapore. The ideal candidate will have expertise in building industry-leading recommendation systems and delivering end-to-end machine learning solutions.

About Us

TikTok is the leading destination for short-form mobile video, inspiring creativity and bringing joy to users worldwide. Our platform connects people from diverse communities, and we strive to create an inclusive space where employees can thrive.

Responsibilities
  • Design and develop cutting-edge recommendation systems to enhance user experience and content ecosystem.
  • Work with cross-functional teams to design product strategies and build solutions to grow TikTok in important markets.
  • Ongoing work involves owning the full stack machine learning system, optimizing algorithms and infrastructure to improve recommendation performance.
Qualifications

To be successful in this role, you should possess:

  • A Bachelor's degree in Computer Science or a related field, with 5+ years of relevant work experience.
  • Strong software development skills in C++, Python, or other programming languages.
  • Experience in applied machine learning, machine learning infrastructure, large-scale recommendation systems, or market-facing machine learning products.
  • Excellent communication and teamwork skills, with a passion for solving challenging problems.
Compensation and Benefits

Our estimated salary range for this position is $180,000 - $250,000 per year, depending on location and experience. Additionally, we offer a comprehensive benefits package, including health insurance, retirement plans, and opportunities for growth and development.

About the Location

This role can be based in either our Los Angeles or Singapore offices, both of which are hubs for innovation and creativity. As a global company, we strive to create an environment that reflects the diversity of our user base and encourages collaboration across cultures and time zones.



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