Tasks:
- * Architect search retrieval and reranking pipelines
- * Build production-scale ML systems
- * Deploy and monitor production ML models
- * Design search relevance and ranking
- * Develop query understanding capabilities
- * Mentor junior engineers and coordinate search improvements
- * Optimize relevance latency and scalability
Perks/Benefits:
Skills/Tech stack required:
[Data Processing] [English] [Faiss] [Information Retrieval] [Intent Classification] [Java] [Learning systems] [Machine Learning] [Machine learning systems] [Mandarin] [ML deployment] [Model Monitoring] [Production ML] [Production ML Deployment] [Query rewriting] [Query Understanding] [Real Time] [Real-time Data] [Real-time Data Processing] [Recommendation Systems] [Search & Ranking] [Search Relevance] [Search Systems] [Semantic Parsing] [Unstructured Data] [Unstructured Data Processing] [Vector Search]
Educational requirements:
N/A
Role(s):
[AI Engineer] [Engineer] [Information Retrieval Engineer] [Learning Engineer] [Machine Learning Engineer] [Retrieval Engineer] [Search Engineer]