Cloud Acceleration Engineer – DPU & AI Infra

What you'll do

• Develop reliable, low-latency DPU network software and collaborate with hardware teams on networking and storage acceleration.
• Explore DPU, GPU, and custom-hardware acceleration for distributed AI training and inference; optimize performance from kernels and drivers to user-space runtimes.
• 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, with an exception for Ph.D. candidates with a 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 (OVS, SR-IOV, eBPF), DPDK, hardware acceleration, NCCL Collectives, or AI/ML infrastructure.

Tools

C/C++, Linux, OVS, SR-IOV, eBPF, DPDK, FPGA, ASIC

AI focus

Explore and optimize AI/ML infrastructure acceleration for distributed training and inference using DPUs, GPUs, and custom hardware; the posting also mentions AI communication patterns, parallelization, inference KV-cache systems, and data preprocessing systems.