Algorithm Engineer

Responsibilities
Develop semantic understanding algorithms for search engine scenarios, including but not limited to:
Query intent recognition and semantic parsing
Semantic relevance modeling between queries and documents/entities
Semantic generalization and error correction for long-tail search scenarios
Semantic understanding for multilingual and multimodal search
Apply and build advanced techniques such as large-scale pre-trained models and graph neural networks to improve the semantic accuracy of search results
Analyze user search behavior data, identify bottlenecks in semantic understanding, and propose optimization strategies
Collaborate with search ranking and knowledge graph teams to deploy algorithms into production systems
Stay up to date with the latest NLP advancements (e.g., Prompt Learning, contrastive learning) and explore their applications in search scenarios
Requirements
Master’s degree or above in Computer Science or a related field, with 3+ years of experience in NLP algorithms
Proficient in Python and deep learning frameworks such as PyTorch or TensorFlow
Hands‑on experience with search engines; familiar with query understanding, intent recognition, and semantic matching
Solid understanding of models such as BERT and Transformer, with experience in large‑scale data training and optimization
Preferred qualifications:
Experience in developing semantic understanding systems for search
Familiarity with search technologies such as Elasticsearch or Lucene
Experience in large model fine‑tuning or distributed training
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