About Us
We are a global technology powerhouse driving the next generation of digital innovation. We don't just adapt to the market—we define it. By combining a highly collaborative, dynamic culture with cutting-edge AI capabilities, we build scalable solutions that connect millions of users worldwide. If you want to work on complex engineering challenges at massive scale, you’ll find your people here.
The Role at a Glance
As a Senior Search & Recommendation Algorithm Engineer, you will be the architect behind our platform’s core discovery engine. Your work will directly move the needle on global growth metrics like Gross Merchandise Volume (GMV), conversion rates (CVR), and user retention.
You will transform massive, multi-modal e-commerce datasets (user behavior, product text, images, and transaction histories) into real-time, hyper-personalized experiences for millions of active shoppers.
Core Responsibilities
- Optimize the Retrieval (Recall) Funnel: Design and scale multi-channel retrieval strategies—including vector search (graph-based approximate nearest neighbors), collaborative filtering, and knowledge graph embedding—to surface high-quality candidates from a catalog of millions.
- Advance the Ranking Pipelines: Develop, deploy, 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 focusing on query intent classification, tokenization, synonym expansion, and semantic entity extraction.
- Personalization & Diversity: Implement sophisticated user interest modeling to capture both long-term preferences and real-time browsing behaviors, masterfully balancing hyper-personalization with item diversity.
- A/B Testing & Evaluation: Formulate rigorous offline evaluation metrics (NDCG, GAUC) and lead online A/B testing frameworks to validate and iterate on live algorithms.
- Scale ML Infrastructure: Partner 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.
What You’ll Bring
Background & Experience
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or a highly quantitative field.
- Experience: 3–5+ years of hands-on experience building large-scale search, recommendation, or digital advertising systems.
- 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.
Technical Toolkit
- Programming: Expert proficiency in Python or C++ with a deep grasp of data structures and algorithmic complexity.
- Deep Learning Stack: Extensive experience with PyTorch or TensorFlow, alongside specialized recommendation libraries (e.g., DeepRec, TorchRec).
- Big Data & Infrastructure: Hands-on experience with Spark, Hive, Flink, and vector databases (e.g., Milvus, Pinecone, Qdrant).
Why You’ll Love It Here
- Massive Scale: Your code will impact millions of users and process trillions of features.
- Innovation First: Access to the latest LLM, NLP, and vector search technologies.
- Collaborative Excellence: Work alongside world-class engineers in a supportive, growth-oriented environment.
Please click on the 'apply' button to apply online. For more information, please reach out to AMIR HAMZAH. (EA: 94C3609 / R1984348)
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