Payment Product builds standardized platform capabilities across payments, refunds, settlement, merchant services, billing, and related financial workflows. The team develops reusable products and infrastructure for multiple businesses, markets, and payment methods.
Payment Solutions partners closely with business and product teams to design and deliver end-to-end payment solutions. The team connects commerce systems with payment platform capabilities, transforming complex requirements into scalable and reliable customer and merchant experiences.
Together, these teams combine deep payment expertise, large-scale distributed systems, and AI-powered engineering to build the next generation of global commerce infrastructure.
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Individuals who are completing or have recently completed a Bachelor's degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
Solid basic knowledge of computer software, understanding of Linux operating system, storage, network IO and other related principles.
Familiar with one or more programming languages, such as Python, Go, and Java. Knowledge of design patterns and coding principles is necessary.
Reliability: Ensuring the reliability and efficiency of our core infrastructure, focusing on system capacity and stability; setting up reliability standards and recovery SOP.
Reliability: Troubleshooting and locating the technical issues, bottleneck analysis, managing system high availability architecture transformation and upgrading.
Efficiency: Building automated operation solutions for large-scale systems; partnering with system development teams for system iteration.
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Payment Product builds standardized platform capabilities across payments, refunds, settlement, merchant services, billing, and related financial workflows. The team develops reusable products and infrastructure for multiple businesses, markets, and payment methods.
Payment Solutions partners closely with business and product teams to design and deliver end-to-end payment solutions. The team connects commerce systems with payment platform capabilities, transforming complex requirements into scalable and reliable customer and merchant experiences.
Together, these teams combine deep payment expertise, large-scale distributed systems, and AI-powered engineering to build the next generation of global commerce infrastructure.
...
Build and evolve the agent runtime (harness / agent loop) powering our creator / seller agents — orchestrate skills, tools, and context.
Engineer context & memory for long multi-turn agents — agentic memory (structured note-taking), context compaction / summarization, context editing / observation masking, and just-in-time (retrieve-then-load) retrieval.
Post-train and fine-tune LLMs (SFT / DPO / RL) and build the data flywheel that turns served conversations into training / eval / retrieval signals.
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Build and evolve the agent runtime (harness / agent loop) powering our creator / seller agents — orchestrate skills, tools, and context.
Engineer context & memory for long multi-turn agents — agentic memory (structured note-taking), context compaction / summarization, context editing / observation masking, and just-in-time (retrieve-then-load) retrieval.
Post-train and fine-tune LLMs (SFT / DPO / RL) and build the data flywheel that turns served conversations into training / eval / retrieval signals.
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Build global logistic and warehousing network, improve operations efficiency and reduce operational cost with data analysis, machine learning and operation research methods.
Create supply chain's data portrait and knowledge graph in various dimensions such as vendors, commodity, place of origin, inventory, production capacity and quality of fulfillment, etc. Predict user needs in different countries and regions, guide inventory preparation and fulfillment, and implement effective pricing. From a comprehensive B-C perspective, optimize platform efficiency and enhance user experience through diverse supply chain collaboration means.
Optimize merchant/merchandise supply. Mine high-quality global merchant leads, establish outreach and conversion mechanism to drive merchant growth, and provide continuous incubation and support after merchant onboard. Track international e-commerce trends and TT content trends, identify best-selling product leads and improve the supply of high-quality and affordable goods. Collaborate with the supply chain system to build systematic product growth and operation capabilities.
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Build global logistic and warehousing network, improve operations efficiency and reduce operational cost with data analysis, machine learning and operation research methods.
Create supply chain's data portrait and knowledge graph in various dimensions such as vendors, commodity, place of origin, inventory, production capacity and quality of fulfillment, etc. Predict user needs in different countries and regions, guide inventory preparation and fulfillment, and implement effective pricing. From a comprehensive B-C perspective, optimize platform efficiency and enhance user experience through diverse supply chain collaboration means.
Optimize merchant/merchandise supply. Mine high-quality global merchant leads, establish outreach and conversion mechanism to drive merchant growth, and provide continuous incubation and support after merchant onboard. Track international e-commerce trends and TT content trends, identify best-selling product leads and improve the supply of high-quality and affordable goods. Collaborate with the supply chain system to build systematic product growth and operation capabilities.
Exploring Cutting-Edge NLP Technologies: From basic tasks like word segmentation and Named Entity Recognition (NER) to advanced business functions like text and multimodal pre-training, query analysis, and fundamental relevance modeling, we apply deep learning models throughout the pipeline where every detail presents a challenge.
Cross-Modal Matching Technologies: Applying deep learning techniques that combine Computer Vision (CV) and Natural Language Processing (NLP) in search, we aim to achieve powerful semantic understanding and retrieval capabilities for multimodal video search.
Large-Scale Streaming Machine Learning Technologies: Utilising large-scale machine learning to address recommendation challenges in search, making the search more personalized and intuitive in understanding user needs.
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Exploring Cutting-Edge NLP Technologies: From basic tasks like word segmentation and Named Entity Recognition (NER) to advanced business functions like text and multimodal pre-training, query analysis, and fundamental relevance modeling, we apply deep learning models throughout the pipeline where every detail presents a challenge.
Cross-Modal Matching Technologies: Applying deep learning techniques that combine Computer Vision (CV) and Natural Language Processing (NLP) in search, we aim to achieve powerful semantic understanding and retrieval capabilities for multimodal video search.
Large-Scale Streaming Machine Learning Technologies: Utilising large-scale machine learning to address recommendation challenges in search, making the search more personalized and intuitive in understanding user needs.
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