Field Applications Engineer, AI Infrastructure

What you'll do

• Debug GPU, driver, networking, and AI software issues across single-node and multi-node environments.
• Support post-sales technical engagements with cloud providers, OEMs, ODMs, and enterprise customers.
• Reproduce customer issues in lab environments by analyzing logs, core dumps, and performance data.
• Collaborate with engineering and product teams to validate fixes and provide field feedback.
• Document debug methodologies and technical findings for internal knowledge bases and best practices.
• Support large-scale cluster bring-up activities and new product introductions.
• Develop and deliver technical training sessions on new products and troubleshooting methodologies.

What we're looking for

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field (preferred).
• Relevant experience gained through internships, co-ops, academic projects, or early-career technical roles (preferred).
• Proficiency in at least one programming language such as Python, C/C++, or Java (preferred).
• Experience with Linux systems, command-line tools, system administration, and low-level debugging using tools such as kernel logs and gdb (preferred).
• Knowledge of CPU and GPU architecture concepts, memory hierarchies, and system-level behavior (preferred).
• Working understanding of GPU architecture and GPU-accelerated workloads, including experience using AMD or NVIDIA GPU platforms (preferred).
• Foundational understanding of server and accelerator architectures, including PCIe topologies, CPU-GPU interconnects, memory hierarchies, and NUMA concepts (preferred).
• Exposure to AI or HPC workloads and familiarity with frameworks such as PyTorch or TensorFlow (preferred).