Recommendation Algorithm Engineer

Responsibilities

• Build and optimize the end-to-end personalized recommendation system for a prediction market platform, covering personalized home feeds, trending events, sports/event markets, and other recommendation scenarios. Drive improvements in key business metrics, including CTR, user engagement time, participation rate, retention, and trading conversion.
• Develop multidimensional user profiles based on browsing, clicks, follows, trades, dwell time, and search behaviors to model user interests, risk preferences, thematic preferences, and activity levels.
• Design and optimize the full recommendation pipeline, including cold start, candidate retrieval, ranking (CTR/CVR prediction), diversity, multi-objective optimization, real-time relevance, and trending content boosting.
• Build a scalable feature engineering framework by mining user behavior sequences and constructing high-quality recommendation features.
• Continuously improve recommendation models and strategies through data-driven iterations, balancing personalization accuracy, content diversity, popularity concentration, and overall user experience. Establish robust evaluation, monitoring, and optimization mechanisms for recommendation performance.

Qualifications

• Bachelor's degree or above in Computer Science, Artificial Intelligence, Data Science, or a related field, with 5+ years of hands-on experience in production search or recommendation systems. Experience in content feeds, news, trending events, financial market data, or community recommendation systems is highly preferred.
• Strong understanding of the complete recommendation system architecture, including candidate retrieval (vector-based, rule-based, and popularity-based), ranking, re-ranking, multi-objective optimization, and cold-start strategies. Proficient in mainstream CTR/CVR prediction techniques.
• Proficient in deep learning frameworks such as PyTorch or TensorFlow, with the ability to independently conduct feature engineering, model training, offline evaluation, online tuning, and iterative experimentation.
• Solid experience in feature engineering, user behavior sequence modeling, and techniques for handling sparse data.
• Strong analytical skills and business understanding, capable of identifying recommendation issues through data analysis (e.g., traffic imbalance, insufficient long-tail exposure, ineffective cold start, content homogenization) and implementing effective optimization strategies.
• Familiar with A/B testing methodologies and experimental design, with the ability to quantitatively evaluate recommendation strategies and drive continuous optimization.

Preferred Qualifications

• Experience in recommendation systems for financial markets, trading platforms, market data, trending events, news feeds, or sports content is highly preferred.
• Experience with vector retrieval (ANN/Embedding Search), personalized ranking lists, real-time trending ranking, and multi-objective recommendation modeling.
• Experience designing user profiling and tagging systems, with the ability to develop recommendation strategies that combine long-term and short-term user interests.