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.
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