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.