Research Engineer/Scientist focused on designing and implementing a high-performance KV cache layer for LLM inference to improve latency, throughput, and cost-efficiency in transformer-based model serving.

Research Engineer/Scientist focused on applying AI/ML to database management systems, including query optimization, indexing, workload forecasting, and developing self-managing databases. The role involves integrating AI models into production systems and publishing research findings.

Research Engineer/Scientist role focusing on applying AI/ML to database management systems, including query optimization, indexing, workload forecasting, and developing self-managing databases. The role involves research and development, integrating AI models into production systems, analyzing large datasets, and publishing findings. Requires a PhD and strong publication record in AI/databases/systems, with experience in database internals and ML frameworks.

Research Engineer/Scientist focused on applying AI/ML to database management systems, including query optimization, indexing, and workload forecasting, with a goal of building AI-native data infrastructure and intelligent optimization. The role involves research and development, integrating models into production, and publishing findings.

Research Scientist focused on DPU and AI infrastructure, aiming to accelerate distributed training and inference by co-designing software and hardware solutions. Explores AI/ML infrastructure acceleration leveraging DPUs, GPUs, and custom hardware.

Research Scientist role focused on designing and developing DPU network software for AI/ML workloads, optimizing distributed training and inference, and exploring software-hardware co-design for cloud and AI computing infrastructure.

Research Scientist role focused on designing and developing DPU network software for AI/ML workloads, including distributed training and inference acceleration, and software-hardware co-design.

This role focuses on designing and developing DPU network software and exploring AI/ML infrastructure acceleration using DPUs, GPUs, and custom hardware to optimize distributed training and inference. It involves software-hardware co-design and end-to-end performance optimization for cloud-scale computing.