Tasks:
- * Architect end to end search systems
- * Build query understanding, retrieval, ranking, and re-ranking pipelines
- * Collaborate cross-functionally on search improvements
- * Deploy machine learning models to production
- * Design and optimize search relevance, ranking, and user experience
- * Develop query rewriting, intent classification, and semantic parsing
- * Develop scalable production machine learning systems for search
- * Mentor junior engineers
- * Monitor and troubleshoot production models
- * Optimize relevance latency and scalability
- * Process large-scale real-time and unstructured data
Perks/Benefits:
Skills/Tech stack required:
[Data Processing] [Faiss] [Information Retrieval] [Intent Classification] [Java] [Large-scale] [Large-scale Machine Learning] [Large Scale Machine Learning Systems] [Learning systems] [Machine Learning] [Machine learning systems] [Model Deployment] [Model Monitoring] [Model Training] [Production Machine Learning] [Query rewriting] [Query Understanding] [Real Time] [Real-time Data] [Real-time Data Processing] [Recommendation Systems] [Re-ranking] [Retrieval systems] [Scalable 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] [Learning Engineer] [Machine Learning Engineer] [Search Engineer] [Senior AI Engineer]