Research Scientist Graduate

We build the recommendation systems behind TikTok Shop’s shoppable videos and LIVE streams in fast-growing markets across Latin America, Canada, Asia, Australia and the new countries we launch each year. AI and LLMs are transforming e-commerce, recommender systems, and the way research scientists work, opening up new problems and opportunities at the frontier. LLMs and agents are core to how we work, not just tools we reach for. Our autonomous research agents already run experiments, tune models and diagnose issues in production, and they drive a remarkable share of the improvements we ship. As more execution is automated, what matters most is knowing what to solve. Beyond building and scaling generative models, we own business outcomes end to end: we find the problems, set the direction and define the goals. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. 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 applies to the company's and its affiliates' jobs globally. Applications are reviewed on a rolling basis; we encourage you to apply early. ## Responsibilities - Build e-commerce recommendation at global scale, end to end. Design and improve recommendation systems for a platform serving over a billion users, across shoppable videos, LIVE streams and products. Work on every stage, from retrieval and pre-ranking to ranking and final blending. - Pioneer generative and LLM-powered recommendation. Use LLMs and generative models to change how recommendation works, including generative retrieval, LLM reasoning about user intent (LLM4Rec), and multimodal understanding of AI-generated content (AIGC). - Optimize decisions with reinforcement learning, causal inference and operations research. Frame traffic allocation, buyer acquisition and compute allocation as optimization problems. Use reinforcement learning, causal and uplift modeling, and constrained optimization to determine where each impression and unit of compute creates the most incremental value. - Build research agents. Create autonomous research agents that mine data for insights, search across competing objectives, run and analyze experiments, and diagnose production issues. - Model the full lifetime of a shopper. Model long-term value, repurchase and retention, purchase attribution across surfaces and over time, content fatigue, new-user recommendation and interest exploration. - Turn research into impact. Own ideas from first prototype to production, validate them through large-scale online experiments, and share findings through top-tier publications. ## Minimum Qualifications - Completing or recently completed a bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline. - Strong foundations in machine learning and its underlying math, including probability, statistics and optimization, along with solid algorithms and data structures. - Excellent coding skills in Python or C++ and hands-on experience with deep learning frameworks such as PyTorch. - Research or project experience in at least one of: recommendation, search or ads; LLMs and generative models; reinforcement learning; causal inference; operations research and optimization; multimodal learning; or AI agents. - Proven ability to take an open-ended, loosely defined problem, frame it precisely, and drive it to a measurable result. ## Preferred Qualifications - Completing or recently completed a master's degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline. - First-author publications at top venues (NeurIPS, ICML, ICLR, KDD, SIGIR, RecSys, WWW, WSDM, ACL, CVPR, EC) or impactful open-source work. - Experience with generative recommendation, LLM4Rec, or LLM post-training (SFT, DPO, GRPO, RL-based fine-tuning). - Experience building LLM agents or automating research workflows, and heavy use of AI coding tools. - Background in reinforcement learning or bandits, causal and uplift modeling, or constrained optimization. - Experience modeling long-term user value, such as retention, user lifetime value or sequential user behavior. ## Compensation and Benefits The base salary range for this position in the selected city is $121,600–$243,200 annually. Compensation may vary based on qualifications, skills, competencies, experience and location. The role may also be eligible for discretionary bonuses or incentives and restricted stock units. Benefits may vary depending on the nature of employment and 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 and wellbeing benefits. 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.