As part of the Content & Growth AI Team, you'll play a central role in shaping content discovery, driving user engagement, and unlocking the next wave of platform growth. From personalized feeds and real-time hot topic detection to AI-generated content (AIGC) strategies, we combine algorithmic excellence with product intuition to amplify impact.
You'll join a world-class team of engineers and researchers focused on building scalable, high-performance AI systems that power content recommendations, trending detection, and multimodal user engagement.
What You'll Do
- Design and optimize large-scale recommendation algorithms to enhance personalized user experiences across feeds, content hubs, and interactive touchpoints.
- Build intelligent content growth pipelines, including real-time hot topic detection, viral content diffusion modeling, and trending topic amplification.
- Develop and integrate AIGC-aware recommendation systems, enabling dynamic ranking and generation strategies based on user preferences and market signals.
- Apply state-of-the-art retrieval, ranking, and re-ranking models to refine recommendation precision, diversity, and freshness.
- Leverage sequence models (Transformers, RNNs) and graph-based methods to model user behavior over time and across content types.
- Employ multi-modal learning (text, image, video, social graphs) to improve understanding of content and boost personalization effectiveness.
- Collaborate cross-functionally with product, data, and infrastructure teams to define and drive content growth strategies aligned with business objectives.
- Run large-scale A/B tests, perform causal inference and behavioral analytics to quantify impact, guide iteration, and scale success.
- Contribute to system architecture, model deployment pipelines, and performance optimization for real-time inference at scale.
- Stay at the frontier of recommendation, generative AI, and content intelligence research, translating innovation into production impact.
Qualifications
- 2+ years of experience in recommendation systems, content AI, or growth-focused machine learning.
- Proven track record in developing large-scale personalized recommendation engines using deep learning, collaborative filtering, or hybrid models.
- Hands-on experience with hot topic mining, entity co-occurrence graph modeling, or event-based content surfacing is a strong plus.
- Solid understanding of retrieval-ranking architectures, cold-start mitigation, and user lifecycle-based personalization.
- Experience with deep learning frameworks (e.g., TensorFlow, PyTorch), vector search (e.g., FAISS, Milvus), and knowledge-enhanced models.
- Proficiency in big data processing (Spark, Hive, Hadoop) and distributed computing frameworks.
- Strong problem-solving and communication skills ability to drive cross-functional collaborations.
- Passion for content ecosystems, user growth loops, and delivering measurable impact through intelligent systems.
- Able to use Chinese and English as working language to work with chinese speaking stakeholders
