Responsible for the development of deep learning and operations research models and related intelligent systems for the supply chain and logistics of the global E-Commerce business.
Participate in building a large-scale graph storage and graph learning platform to improve relationship building and governance within the e-commerce community.
Lead the enablement of new machine learning frameworks and GPU platforms, driving large-scale production deployment while maintaining model quality and business performance;
Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
Establish adoption frameworks and ROI tracking mechanisms for deployed AI products (e.g., reduction in fraud losses, 30% content operations efficiency gain).
Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Next-Gen Marketing Tech: Go beyond traditional discriminative uplift prediction. Research and productionize cutting-edge causal inference and AI technologies — including Uplift Modeling, E2E Modeling, RL, LLMs, and AI Agents — to directly address user conversion and repeat purchase behavior.
Develop labeling systems that support dataset segmentation, coverage analysis, quality filtering, training-data mixtures, and model performance attribution.
Participate in the exploration and development of personalized AI search, with a focus on harness engineering, post-training optimization, and rubric-based automated evaluation, to build next-generation search experiences that better understand users, reason over intent, and make intelligent decisions.
Model Deployment & Fusion. Bridge the gap between AI research and production. Optimize deep learning models for efficient inference and ensure seamless integration of compression algorithms into deployment frameworks (e.g., vLLM).
Lead the implementation and operationalisation of knowledge base and retrieval-augmented generation solution stacks to support scalable GenAI and agentic use cases across business domains
Collaborate closely with Product, ML/Algorithm, and Design teams to align prompts with product feature logic, ensuring quality and stability across critical user journeys.