about the company. A premier global technology enterprise known for pioneering advancements and market-leading digital innovation. The organization focuses on scalable growth and expanding its intelligent capabilities within a dynamic and collaborative culture.
Role OverviewAs a Senior Search & Recommendation Algorithm Engineer, you will directly impact the platform's core growth metrics like Gross Merchandise Volume (GMV), conversion rates (CVR), and user retention. You will design, build, and optimize the machine learning algorithms that power our search marketplace and personalized recommendation feeds. This role involves turning complex, multi-modal e-commerce data (user behavior, product text, images, and transaction histories) into real-time personalized experiences for millions of active shoppers.
Core Responsibilities
... - Optimize the Retrieval (Recall) Funnel: Design multi-channel retrieval strategies including vector search (graph-based approximate nearest neighbors), collaborative filtering, and knowledge graph embedding to surface high-quality candidate items from a catalog of millions.
- Advance the Ranking Pipelines: Develop and maintain state-of-the-art Deep Learning models for Click-Through Rate (CTR) and Conversion Rate (CVR) prediction.
- Enhance Query Understanding: Build advanced NLP and Large Language Model (LLM) pipelines for e-commerce search queries, focusing on query intent classification, tokenization, synonym expansion, and semantic entity extraction.
- Personalization & Diversity: Implement user interest modeling to capture long-term preferences and short-term real-time browsing behaviors, balancing personalization with item diversity to prevent echo chambers.
- A/B Testing & Evaluation: Formulate rigorous offline evaluation metrics (NDCG, GAUC) and lead the deployment of online A/B testing frameworks to validate algorithm iterations.
- Scale Machine Learning Infrastructure: Collaborate with data and MLOps teams to build high-throughput, low-latency online inference services and optimize large-scale distributed training on trillions of sparse features.
Key Requirements
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or a highly quantitative field.
- Industry Experience: Minimum of 3–5 years of hands-on experience in large-scale search, recommendation systems, or digital advertising systems.
- Programming Skills: Expert proficiency in Python or C++, with deep knowledge of data structures and algorithmic complexity.
- Machine Learning Stack: Extensive experience with deep learning frameworks (PyTorch or TensorFlow) and specialized recommendation libraries (e.g., DeepRec, TorchRec).
- Big Data & Distributed Computing: Hands-on experience with Spark, Hive, Flink, and vector databases (e.g., Milvus, Pinecone, or Qdrant).
- E-commerce Domain Knowledge: Strong familiarity with e-commerce concepts like multi-task learning (e.g., MMOE, ESMM), cold-start strategies, and graph neural networks for item matching.
Please click on the 'apply' button to apply online. For more information, please reach out to AMIR HAMZAH. (EA: 94C3609 / R1984348)
