Architecture for data at the scale of hundreds of billions: Conducting in-depth research and innovation in all aspects, from large-scale offline computing and performance and scheduling optimization of distributed systems to building high-availability, high-throughput, and low-latency online services.
Recommendation Technologies: Leveraging ultra-large-scale machine learning to build industry-leading search recommendation systems and continuously explore and innovate in search recommendation technologies.
Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
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Individuals who are completing or have recently completed a PhD degree in Electrical Engineering, Thermal Engineering, Energy & Power, Materials, Refrigeration, Environmental Engineering, Mechanical Engineering, or related fields are preferred.
Possess excellent problem analysis and solving skills, an innovative and rigorous mindset, and the ability to independently overcome technical difficulties.
Possess independent research capabilities for theoretical and applied research. Familiar with liquid cooling, thermal management, hydrophilic coatings, renewable energy, and energy storage, keeping track of cutting-edge industry technologies.
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Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm.
Work with the relevant software platforms in which the models are deployed.
Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
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Strong coding skills in at least one of the following programming frameworks, e.g. Python, Java, C/C++, Spark etc.; Familiar with at least one of the following deep learning frameworks, e.g., PyTorch, TensorFlow, Keras etc.
Solid data structure/algorithm foundation, proficient in machine learning/deep learning theoretical knowledge, and rich practical experience.
Familiar with NLP, sentiment analysis, CV, multimodality, graph algorithm, search algorithm, text/data mining in 1-2 areas.
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Design and build supporting/debugging tools as needed.
Support the production of scalable and optimised AI/machine learning (ML) models.
Focus on building algorithms for the extraction, transformation and loading of large volumes of realtime, unstructured data to deploy AI/ML solutions from theoretical data science models.
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Problem Solving for Governance Applications: Address challenges such as long text/sequence modeling, few-shot learning, content moderation, violation detection, and policy recommendation using large models and multimodal approaches.
Model Development and Optimization: Research and optimize e-commerce-specific NLP and multimodal large models to improve multilingual, multi-task, and multi-modal algorithm performance across various e-commerce scenarios.
Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
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Explore the integration of LLMs / VLMs with recommendation systems to develop adaptive and evolving intelligent recommenders.
Research end-to-end generative recommendation and system optimization methods that balance efficiency and user experience.
Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
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Possessing excellent programming skills, proficient in at least one programming language such as Python/C++, and proficient in at least one deep learning framework such as TensorFlow/PyTorch.
Familiar with common model structures such as Attention, Transformer, BERT, GPT, and familiar with the principles and implementation of open-source large models such as LLaMA, ChatGLM. Priority will be given to those with experience in fine-tuning and training open-source models.
Excellent ability to analyze and solve problems, passionate about solving challenging problems, and good communication and teamwork skills.
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Participate in building a large-scale graph storage and graph learning platform to improve relationship building and governance within the e-commerce community.
Explore and research cutting-edge technologies in machine learning/graph learning/sequence learning and related fields, implement these technologies in practical business scenarios, and support the production of scalable and optimised machine learning models.
Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
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Work in a cross-functional team setting to mitigate business risks.
Work with relevant software platform to develop/deploy/monitor the models.
Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
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Participate in building a large-scale graph storage and graph learning platform to improve relationship building and governance within the e-commerce community.
Explore and research cutting-edge technologies in machine learning/graph learning/sequence learning and related fields, implement these technologies in practical business scenarios, and support the production of scalable and optimised machine learning models.
Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
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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.
Utilize e-commerce big data and deep learning models to predict end-to-end estimated time of arrival (ETA), and some logistics events such as failed delivery, delivered but not received to enhance the user logistics experience. Build logistics network knowledge graphs and predict the spatio-temporal trajectory sequence of express packages through deep learning, statistical inference and other algorithmic methods. Use NLP and LLM algorithms to handle address problems such as address verification and address suggestion.
Utilize time series forecasting techniques to predict sales at different granularities and horizons, such as warehouse-level manpower forecasting, inventory-level demand forecasting etc. We need strong machine learning and deep learning skills to detect important factors and model the relationship between the future and history.
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Construct multimodal semantic links across video-product, product-product, and video-video relationships to support trend understanding, product mining, AIGC generation, and content-commerce supply optimization.
Explore next-generation generative search and recommendation through full-format representation learning and SID modeling across videos, livestreams, products, and queries.
Design and develop generative algorithms tailored for high engaging, high conversion e-commerce visual creatives, solving challenges in identity-preserving and aesthetic style consistency to produce production-grade intelligent background synthesis,virtual try-on, and localised lifestyle scene generation.
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Root cause analysis of heterogeneous data: Large models must automatically identify fault root causes and build knowledge from massive heterogeneous monitoring data, requiring high model understanding and generalization capability.
Technology implementation and adaptation: Hardware innovation must comply with policies and fit industry needs, while AI operations must integrate with existing platforms and tools. Successfully combining and implementing these poses significant challenges.
Solve key technical problems in data centers and increase global competitiveness.
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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.
Pioneer the development of domain-specific LLMs by leveraging massive e-commerce data for Continual Pre-Training (CPT), Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL). Design and deploy intelligent AI Agents based on an "Agent + Skill" framework to autonomously diagnose and resolve complex user-facing and operational issues.
Utilize e-commerce big data and deep learning models to predict end-to-end estimated time of arrival (ETA), and some logistics events such as failed delivery, delivered but not received to enhance the user logistics experience. Build logistics network knowledge graphs and predict the spatio-temporal trajectory sequence of express packages through deep learning, statistical inference and other algorithmic methods. Use NLP and LLM algorithms to handle address problems such as address verification and address suggestion.
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