General Machine Learning Scientist Graduate ( Recommendation PhD

Machine Learning Scientist Graduate (Tik Tok Recommendation)

Location:

San Jose

Employment Type:

Regular

Job Code: A73000

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 fast-pacing, collaborative and impact-driven. This opening is part of the general hiring process for the Tik Tok Recommendation organization.

Applications will be evaluated by multiple teams within the Recommendation organization to ensure the best fit based on skills and interests. We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.

• Build industry-leading recommendation system, improving user experience, content ecosystem and platform security;
• Deliver end-to-end machine learning solution to address critical product challenges;
• Own the full stack machine learning system and optimize algorithms and infrastructure to improve recommendation performance.
• Work with cross functional teams to design product strategies and build solutions to grow Tik Tok in important markets.

Minimum Qualifications:

• Individuals who are completing or have recently completed a PhD degree in computer science, machine learning, or similar fields.
• Good knowledge of theoretical and empirical research in addressing research problems
• Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch, Tensor Flow) and familiarity with deep neural network architectures

Preferred Qualifications:

• Research experience in one or more of the following fields: applied machine learning, machine learning infrastructure, large-scale recommendation system, market-facing machine learning product.
• Strong first-author publications record in major AI conferences or journals(e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL etc.)
• Proficient in C/C++, Python, and shell programming languages, and have a deep understanding of data structure and algorithm design.
• Internship experience in an AI research organization

The base salary range for this position in the selected city is $156000 - $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.

Tik Tok is the leading destination for short-form mobile video. At Tik Tok, our mission is to inspire creativity and bring joy. Tik Tok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Inspiring creativity is at the core of Tik Tok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our…