Search Intelligence Researcher

7 days ago

Singapore beBeeSearch Full time $125,000 - $175,000
About the Role:
The Search Intelligence team is responsible for driving innovation in search algorithm and architecture research and development (R&D). We leverage cutting-edge machine learning technologies to improve end-to-end modeling, and continuously push for breakthroughs.

We focus on the construction and performance optimization of distributed and machine learning systems. This includes memory and disk optimization, index compression, recall and ranking algorithms, and providing ample opportunities for growth and development.

Main Areas of Work:

Cutting-Edge NLP Technologies:

Our team applies deep learning models throughout the pipeline, tackling challenges such as word segmentation, Named Entity Recognition (NER), text pre-training, query analysis, and fundamental relevance modeling.


Cross-Modal Matching Technologies:

We combine Computer Vision (CV) and Natural Language Processing (NLP) techniques to achieve powerful semantic understanding and retrieval capabilities for multimodal video search.


Large-Scale Streaming Machine Learning Technologies:

We utilize large-scale machine learning to address recommendation challenges in search, making it more personalized and intuitive in understanding user needs.


  • Architecture for data at the scale of hundreds of billions: Conducting in-depth research and innovation in all aspects, from large-scale offline computing and performance scheduling optimization of distributed systems to building high-availability, high-throughput, and low-latency online services.

Recommendation Technologies:

We leverage ultra-large-scale machine learning to build industry-leading search recommendation systems and continuously explore and innovate in search recommendation technologies.


Specific Objectives:

  • Exploring the integration of large models with ranking algorithms to improve the accuracy of personalized ranking and user experience.
  • Researching generative retrieval algorithms to solve ultra-large-scale retrieval problems in candidate libraries with tens or hundreds of billions of entries.
  • Leveraging large language models (LLMs) to enhance search satisfaction for complex and polysemous queries.
  • Building high-performance, low-resource-consumption large-scale batch-stream integrated retrieval and computing systems to improve resource utilization.

Key Challenges:

  • Challenges in Personalized Ranking

Traditional ranking algorithms struggle to fully utilize multimodal information (e.g., text, images, video) and have limited model complexity, failing to meet users' demands for precise and personalized search results.


  • Challenges in Ultra-Large-Scale Retrieval

In retrieval scenarios with candidate libraries containing hundreds of billions of entries, traditional discriminative models face issues such as insufficient model capacity and low indexing efficiency, urgently requiring next-generation retrieval algorithms.


  • Challenges in Complex Query Understanding

User search intents are becoming increasingly complex. Traditional search engines struggle to accurately interpret the semantics of long/complex sentences and polysemous queries, leading to low satisfaction with search results.


  • Challenges in Resource Utilization

The storage-computation separation architecture of search systems results in low resource utilization. Optimizing resource usage while maintaining performance has become a critical issue.


Necessity of Large Model-Based Intelligent Search:

Introducing large model technologies is essential to address the above challenges. It can significantly enhance the semantic understanding, retrieval efficiency, and resource utilization of search systems, thereby delivering more accurate and efficient search experiences to users.

Qualifications:

  • A PhD degree in AI, computer science, natural language processing, computer vision, or related fields.
  • Publishable papers at top AI conferences or significant research experience.
  • Solid foundation in machine learning/deep learning algorithms and proficient coding skills in C/C++ or Python.
  • Intelligent, confident, and passionate about technology, with good team communication and collaboration skills.

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