General Machine Learning Engineer Graduate ( Recommendation BS​/MS

Location:

San Jose

Employment Type:

Regular

Job Code: A175175

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. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally.

Applications will be reviewed on a rolling basis – we encourage you to apply early.

• 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.

• Individuals who are completing or have recently completed a Bachelor’s or Master’s degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Proficient coding skills and strong algorithm & data structure knowledge using C++/Python or other programming language.
• Experienced in Machine Learning, familiar with at least one Deep Learning framework, e.g. PyTorch, Tensor Flow.
• Experienced in one or more of the following areas, e.g. NLP, CV, Recommender System, and Machine Learning.
• Effective communication and teamwork skills.

• Authors of published papers in accredited academia conferences is an advantage.
• Winners of algorithm and machine learning competitions such as ACM and Kaggle is an advantage.

The base salary range for this position in the selected city is $122,574 - $256,000 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.

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: