Responsibilities Team Introduction Global E-Commerce Content Recommendation 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. In today's content-driven commerce landscape, traditional collaborative filtering and supervised learning methods are no longer sufficient. We're actively exploring how large language models (LLMs) and generative AI can fundamentally transform the recommendation process: from retrieval to ranking, and from static listings to dynamic, generative user-item interactions. 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 TikTok. 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 TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities - Develop and deploy ML models to power personalized e-commerce recommendations - Collaborate cross-functionally with product, infra, and data teams to translate business goals into technical solutions - Evaluate model performance in both offline and online (A/B) testing to drive user experience and GMV - Focused on scaling, robustness, and production-quality deployment Qualifications Minimum Qualifications: - Final year with a background in Software Development, Computer Science, Computer Engineering, or a related technical discipline. - Proficient coding skills in Python and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch - Demonstrated ability to conduct rigorous research and analyze large-scale data - Strong problem-solving skills and a high sense of ownership Preferred Qualifications: - Publications in ML/AI conferences (e.g., NeurIPS, ICML, ACL, SIGIR, KDD, CVPR, RecSys) - Experience with recommendation systems, retrieval models, or multi-modal learning - Familiarity with building and deploying real-time, scalable ML systems in production - Background in e-commerce or related applied AI research domains Job Information [For Pay Transparency]Compensation Description (Annually) The base salary range for this position in the selected city is $124717 - $243200 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.