Cloud Acceleration Engineer

Responsibilities

• Design and develop high-performance, low-latency, reliable DPU network software.
• Collaborate with hardware teams on software-hardware co-design for networking and storage acceleration.
• Explore DPU, GPU, and custom-hardware acceleration for distributed AI/ML training and inference.
• Optimize performance end to end across operating system kernels, drivers, and user-space runtime systems.
• Contribute to architecture design, technical proposals, and long-term research directions.

Requirements

• Bachelor’s or master’s degree in Computer Science, Computer Engineering, or a related field, or a Ph.D. with strong research and publications.
• At least 2 years of relevant industry experience, with an exception for Ph.D. candidates with a strong background.
• Proficiency in C/C++ development and debugging.
• Strong Linux systems development experience.
• Solid understanding of compute architecture, network architecture, and operating systems.
• Background in software-hardware co-design, distributed systems, high-performance networking, or AI/ML systems.
• Preferred experience with network virtualization using OVS, SR-IOV, or eBPF.
• Familiarity with DPDK and high-performance user-space networking.
• Preferred hardware acceleration experience with FPGA, ASIC, GPU, or CUDA.
• Experience with NCCL collectives, AI communication patterns, parallelization techniques, or AI/ML infrastructure such as inference KV-cache and data-preprocessing systems.