Building next-generation generative AI infrastructure for search, advertising, and recommendation businesses.
Through the co-design of large models, multimodal technologies, and system-level innovations, we aim to overcome performance bottlenecks and enable ultra-long context handling, millisecond-level response latency, and high-precision information understanding, thereby driving intelligent upgrades across the business.
Individuals who are completing or have recently completed a PhD degree in Artificial Intelligence, Software Development, Computer Science, Computer Engineering or a related discipline.
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Design, build and optimize distributed training infrastructure and low-latency online inference systems for large-scale recommendation models and Large Language Models
Develop high-performance GPU kernel implementations and efficient inter-node communication primitives to improve training and inference efficiency
Build compiler optimization passes and operator fusion technologies for deep learning frameworks to accelerate model execution
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Conduct novel research on distributed training and inference system optimization for large-scale recommendation models and Large Language Models
Design and implement high-performance GPU kernel architectures and communication primitives to accelerate deep learning workloads
Publish original research findings at top-tier academic conferences and collaborate with cross-functional teams to translate research into production impact
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Build business requirement dashboards.Take on data dashboard building requests across all business directions, and deliver accurate and actionable data.
Support business decision-making through data analysis, identify efficiency bottlenecks and quality issues, and produce actionable improvement recommendations.
Support scientific validation in automated data production, including but not limited to the scientific rigor of datasets and the reasonableness of implementation verification.
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Build business requirement dashboards.Take on data dashboard building requests across all business directions, and deliver accurate and actionable data.
Support business decision-making through data analysis, identify efficiency bottlenecks and quality issues, and produce actionable improvement recommendations.
Support scientific validation in automated data production, including but not limited to the scientific rigor of datasets and the reasonableness of implementation verification.
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