Cloud Acceleration Engineer – DPU & AI Infra

What you'll do

• Design high-performance, low-latency, reliable DPU network software and optimize performance across kernels, drivers, and user-space runtimes.
• Collaborate with hardware teams on networking and storage acceleration, and explore DPU, GPU, and custom-hardware acceleration for AI/ML workloads.
• Contribute to architecture design, technical proposals, and long-term research directions.

What they ask for

• B.S./M.S. in Computer Science, Computer Engineering, or a related field; or Ph.D. with strong research/publications.
• 2+ years of relevant industry experience; exception for Ph.D. with strong background.
• Proficiency in C/C++ development and debugging, and strong Linux systems development experience.
• Solid understanding of compute, network architecture, and operating systems; background in at least one of software-hardware co-design, distributed systems, high-performance networking, or AI/ML systems.

Nice to have

• Ph.D. in a related field with research training and publications.
• Experience with software-hardware co-design, network virtualization, DPDK, hardware acceleration, NCCL Collectives, or AI/ML infrastructure.

Tools

C/C++, Linux, DPDK, RDMA, OVS, SR-IOV, eBPF, CUDA

AI focus

Explore DPU, GPU, and custom-hardware acceleration to optimize distributed AI/ML training and inference; preferred experience includes AI/ML infrastructure such as inference KV cache and data preprocessing systems.