Responsibilities
1. End-to-End Search Algorithm R&D and Optimization
- Design and iterate the algorithm system for overseas large-screen content search, covering user intent understanding, multilingual query parsing, multi-path recall, fine ranking, and re-ranking.
- Continuously improve core metrics, including search CTR, play conversion rate, and search satisfaction.
2. End-to-End Personalized Recommendation Algorithm Development
- Build a dual-layer user profile system that distinguishes the household group from individual members.
- Own end-to-end algorithm design and iteration across the recommendation pipeline: recall, coarse ranking, fine ranking, and re-ranking.
- Develop personalized recommendation models for home viewing and leisure scenarios. These models should improve content matching accuracy, recommendation CTR, session duration, play depth, and user engagement.
- Mine users’ long-term and short-term interests, and solve recommendation challenges such as conflicting interests among family members and cold start.
3. Unified Search & Recommendation Architecture
- Lead the design and implementation of an integrated architecture for search and real-time recommendation.
- Unify content representation, user modeling, and ranking systems so that search intent and personalized recommendation work together closely.
- Enable two-way linkage: search behavior feeds into recommendation, and search intent can be triggered from within recommendation scenarios.
4. Global Multilingual Search & Recommendation Capabilities
- Optimize cross-lingual semantic retrieval, multilingual intent understanding, and unified content vector representation for global markets.
- Ensure accurate, consistent, and intelligent results for both voice and text search.
- Support personalized recommendation in multilingual scenarios.
5. Data Closed Loop & Experimentation
- Work with product, data, and engineering teams to build a full data closed loop for both search and recommendation. The loop should cover exposure, clicks, conversions, and negative feedback.
- Continuously validate strategies through A/B testing to iterate algorithm models and business strategies.
Basic Qualifications
- Bachelor’s degree or above in Computer Science, Mathematics, Statistics, Software Engineering, or a related field. A Master’s degree is preferred.
- 5+ years of industry R&D experience in search, recommendation, or NLP algorithms.
- Proven experience deploying and optimizing large-scale online search or recommendation systems end to end. Experience in joint search-recommendation modeling is a plus.
Algorithm & Engineering Skills
- Solid foundation in machine learning and deep learning. You should be familiar with mainstream search and recommendation models, such as two-tower recall, DeepFM, Wide & Deep, DIN, and multi-objective ranking.
- Proficiency in Python, with hands-on experience in at least one deep learning framework (PyTorch or TensorFlow).
- Familiarity with search engines and vector databases such as Elasticsearch, Faiss, and Milvus, including online tuning and deployment.
- Familiarity with feature engineering, user and content profile systems, and real-time and offline computing frameworks (Flink, Spark). Experience in recommendation features and user behavior sequence modeling is expected.
Business & Experimentation Skills
- Familiarity with core search and recommendation metrics (CTR, CVR, Recall, NDCG, etc.) and A/B testing methodology.
- Strong data analysis, problem diagnosis, and root-cause analysis skills.
- Hands-on experience with large-scale online search and recommendation systems, including independently leading module iteration and performance optimization.
Preferred Qualifications
- Algorithm experience in search and recommendation for overseas large-screen devices, smart home terminals, or streaming content is a strong plus.
- Experience with multilingual NLP, cross-lingual retrieval, or global search and recommendation systems.
- Experience in family multi-user modeling, scenario-aware personalized recommendation, LLM retrieval augmentation (RAG), or unified search-recommendation modeling.
About VIDAA
VIDAA is a leading TV OS distributor, pioneering the next-generation platform-to-the-home. Starting with a Smart TV OS, we aim to encompass various aspects of consumers' digital and connected lives. With millions of households reached daily, we are looking for people that want to drive excellence and innovation.
We are a diverse and passionate company looking for the best and the brightest people to lead sucees. We value people and their needs and we provide a safe and secure environment for everyone. If you are self-motivated, entrepreneurial and know how to thrive in a fast growing environment, then we should talk.