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: You own the models behind TikTok Shop's product, short-video, and livestream recommendation — retrieval, pre-ranking, ranking, re-ranking, and multi-queue blending — and every iteration ships to real traffic.
Core threads:
- Ranking and retrieval models.
Iterate the model architectures that carry the funnel: multi-task and multi-objective learning from click through conversion and GMV;
multi-scenario, multi-format joint modelling;
and the sample, label, and debiasing design that decides what the model actually learns.
- User interest modelling.
Ultra-long behavior sequences (10K+ events) with real trade-offs between positional-encoding extrapolation, attention cost, and online latency and storage;
decoupling stable preferences from seasonal demand, momentary impulses, and needs already satisfied;
and mining implicit negative feedback — impressions without clicks, consecutive skips, fast swipes — at every stage of the funnel.
- Multimodal representation learning. Combine product images, text, and video content with behavioural data into high-quality item and user representations.
- Re-ranking, blending, and exploration.
List-level decisions rather than pointwise scores: multi-queue blending across content types, diversity and repetition control, and exploration mechanisms that surface interests users have not yet expressed — while breaking the recommendation feedback loop.
- Design the cold-start and content-ecosystem mechanisms: New products, new livestream hosts, and new creators arrive in volume every day.
- Do original work on open problems.
Long-term value modelling, repurchase and retention, transaction attribution, fatigue modelling, new-user recommendation, incremental value modelling, LLM4Rec — the frontier problems of a real recommendation system, where the industry has no standard answers.