AI Search Algorithm Engineer

Beijing Foreign Enterprise Management Consultants Co.,Ltd.; Singapore; Vor Ort; SGD 120.000 - 200.000; Vollzeit

Zusammenfassung

Huawei is seeking a Search and Recommendation Algorithm Expert to drive the development and deployment of traditional search systems while integrating LLM-powered enhancements for global search scenarios. You will lead both traditional and generative AI search pipelines, focusing on recall, relevance, and user engagement.

The role requires deep ML/DL knowledge, experience with PyTorch/TensorFlow, and a track record in search and recommendation research or industry implementations.

Qualifikationen

• Master's or PhD in CS/AI/Data Science or related field.
• Strong foundation in ML/DL and IR/recSys concepts.
• Proficiency in Python and at least one major DL framework.
• Experience with large-scale distributed training preferred.

Aufgaben

• Lead development and deployment of traditional search systems, optimizing retrieval and ranking; integrate LLM approaches with search architectures.
• Lead generative AI search pipeline design, with LLM data augmentation, RAG, and tool invocation for enhanced results.

Kenntnisse

Search systems

Recommendation systems

ML/DL fundamentals

Python

PyTorch

TensorFlow

Distributed training

NLP/LLM

Research experience

Ausbildung

Master's or PhD in CS/AI/Data Science

Tools

PyTorch

TensorFlow

Distributed systems

Jobbeschreibung

On behalf of Huawei, a world-renowned information and communication technology company, we are seeking passionate and talented individuals to join our team as Search and Recommendation Algorithm Expert.

Job Description

• Lead the engineering development and production deployment of the full traditional search systems, driving continuous optimization across query understanding, multi-stage retrieval, relevance modeling, and personalized ranking. Integrate LLM technologies with traditional search architectures to enhance semantic understanding for global search scenarios, improving search recall, relevance, and user engagement metrics.
• Lead the engineering development and production deployment of the full generative AI search pipeline. Leverage deep expertise in LLM and RAG retrieval to architect and integrate task planning, tool orchestration, memory management, and result self‑verification capabilities. Optimize hybrid indexing and knowledge segmentation strategies to improve AI-generated search quality, relevance, and click‑through performance.

Requirements

• Master's or PhD degree in Computer Science, Artificial Intelligence, Data Science, or other related fields.
• Solid foundation in machine learning and deep learning, with solid understanding of fundamental theories and methods in information retrieval, search and recommendation systems, and natural language processing/large language model etc.
• Proficiency in at least one programming language among Python, C++, or Java, and familiarity with mainstream deep learning frameworks such as PyTorch and TensorFlow. Experience in large-scale distributed training is preferred.
• In-depth research or industrial implementation experience in search-related areas, including but not limited to semantic query understanding, multi-stage retrieval and ranking, relevance filtering, multi-objective ranking, LLM data augmentation, RAG retrieval augmentation, AI Agent task planning and tool invocation.
• Strong analytical and problem-solving skills, with the ability to efficiently identify search quality issues from large-scale search logs, develop multi-objective optimization strategies, and balance key performance metrics including retrieval recall, search relevance, user click-through rate, and model instruction-following capability.
• Excellent teamwork and communication skills, with the ability to collaborate effectively with engineering, product, research, and cross-functional teams to drive successful project implementation.
• Candidates with 2~3+ years of R&D experience in search algorithms (content/news/document/application search, etc.) is preferred. Strong business awareness, ownership, and a passion for developing impactful AI products will be considered valuable advantages.