Machine Learning Engineer Graduate - E-Commerce Recommendation Video - 2027 Start

## Team Introduction Global E-Commerce (TikTok Shop) is one of TikTok's fastest-growing businesses and a core driver of the company's revenue growth. Our team, Global E-Commerce Content Recommendation, owns the end-to-end recommendation stack for e-commerce video and image-text content on TikTok worldwide — retrieval, ranking, and multi-queue blending; supply ecosystem and cold start; and the browsing-to-purchase experience for hundreds of millions of users. We are building what we intend to be the most advanced recommendation system in the world, on top of a two-sided marketplace that is still growing fast. Many of the problems that matter most here — what to optimize, how to know it worked, how to treat a brand-new seller fairly — have no settled answer anywhere in the industry. 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 is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. ## Responsibilities - Optimize the recommendation models across the full funnel, including retrieval, pre-ranking, ranking, re-ranking, and multi-queue blending for TikTok Shop's product, short-video, and livestream recommendations. - Iterate ranking and retrieval model architectures, including multi-task and multi-objective learning, multi-scenario and multi-format joint modelling, and sample, label, and debiasing design. - Develop user interest modelling for ultra-long behavior sequences, stable preferences, seasonal demand, momentary impulses, satisfied needs, and implicit negative feedback. - Build multimodal representation learning systems that combine product images, text, video content, and behavioural data. - Work on re-ranking, blending, and exploration, including list-level decisions, multi-queue blending, diversity and repetition control, and exploration mechanisms. - Design cold-start and content-ecosystem mechanisms for new products, livestream hosts, and creators. - Conduct original work on open problems including long-term value modelling, repurchase and retention, transaction attribution, fatigue modelling, new-user recommendation, incremental value modelling, and LLM4Rec. ## Minimum Qualifications - Completing or recently completed a Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related discipline. - Solid algorithms and data-structures fundamentals and excellent coding ability. - Solid foundations in machine learning, probability, and statistics. - Fluent in PyTorch or TensorFlow and at least one large-scale data-processing tool such as Spark, Flink, or SQL. - Proficient in Python, with working C++ and Linux skills. - Data sense and experimental rigor, including understanding of A/B test design, statistical confidence, and common pitfalls. - Fast learner, clear communicator, and good collaborator. ## Preferred Qualifications - Research or engineering experience in recommendation, search, ads, information retrieval, NLP, or large-scale ML systems. - Publications at KDD, NeurIPS, WWW, SIGIR, WSDM, ICML, ICLR, RecSys, CIKM, or comparable venues, or high-quality open-source contributions. - Awards in Kaggle, Tianchi, or RecSys Challenge, or an ACM-ICPC or NOI competition background. - Hands-on experience with causal inference, online learning or bandits, graph neural networks, sequence modelling, or large-scale distributed training. - Heavy user of AI coding and agentic workflows for building systems and optimizing models. ## Compensation and Benefits The base salary range for this position in the selected city is $128,000 - $316,800 annually. The role may be eligible for additional discretionary bonuses or incentives and restricted stock units. Benefits include 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; 10 paid holidays per year; 10 paid sick days per year; and 17 days of Paid Personal Time.