Algorithm Engineer (Search/ Recommendation )

About the role

We are seeking a Search & Recommendation Algorithm Engineer to drive end-to-end algorithm R&D and system architecture for our overseas AIOS platform. You will build high-precision, multi-lingual, and family-scenario-aware search and recommendation systems targeting global household users. This role requires deep expertise in large-scale online systems, machine learning models, and joint search-recommendation modeling.

Key responsibilities

• Design and iterate full-link search algorithms including intent understanding, multi-lingual query parsing, multi-path recall, fine ranking, and re-ranking to optimize CTR, conversion, and satisfaction metrics
• Build a dual-layer user profiling system (Household Group + Individual Member) and develop recommendation models across recall, coarse ranking, fine ranking, and re-ranking stages
• Solve multi-user interest conflicts and cold-start challenges in home viewing scenarios
• Lead the architecture evolution uniting real-time recommendation and search, standardizing content representations, user modeling, and ranking systems
• Enable bidirectional data flow between search intent and recommendations
• Optimize cross-lingual semantic retrieval, multi-lingual query processing, and unified vector embeddings for voice and text search across global markets
• Build end-to-end data loops (exposure-click-conversion-feedback) and run continuous A/B testing to validate strategy performance and iterate algorithms

About you

• Bachelor's degree or higher (Master's preferred) in Computer Science, Math, Software Engineering, or related fields
• 5+ years of R&D experience in Search, Recommendation, or NLP algorithms with proven deployment in large-scale online systems
• Strong foundation in deep learning models such as Two-Tower Recall, DeepFM, Wide&Deep, DIN, and Multi-Objective Ranking
• Proficient in Python and at least one framework (PyTorch or TensorFlow)
• Practical experience with search engines and vector databases (Elasticsearch, Faiss, Milvus)
• Solid experience in feature engineering, user behavior sequence modeling, and real-time/offline computing frameworks (Flink / Spark)
• Familiarity with evaluation metrics (CTR, CVR, Recall, NDCG) and statistical A/B experimentation methodologies
• Prior search/recommendation algorithm experience on TV platforms, smart home terminals, or streaming media services (preferred)
• Expertise in multi-lingual NLP, cross-lingual retrieval, LLM retrieval augmentation (RAG), or household multi-user modeling (preferred)