Field Applications Engineer, AI Infrastructure

About the Role

We are seeking a motivated and technically curious Field Applications Engineer to join our Global Partner Support team. This role is ideal for an early career engineer who is motivated to grow deep expertise at the intersection of data center hardware, system software, and AI workloads. You will work alongside experienced FAEs and engineering teams to debug, triage, and resolve complex issues involving AMD Data Center GPUs and the AI software stack. This is a hands-on role designed for engineers who enjoy learning through real-world problem solving in fast-paced, high-impact environments.

Key Responsibilities

• Participate in system-level debugging of GPU, driver, networking, and AI software issues across single-node and multi-node environments, progressively taking ownership of complex debug efforts.
• Support post-sales technical engagements with cloud providers, OEMs, ODMs, enterprise customers, and strategic partners.
• Reproduce customer and partner issues in lab environments and analyze logs, core dumps, performance data, and system behavior to determine root causes.
• Collaborate closely with engineering, product, and validation teams to drive issue resolution, validate fixes, and provide critical field feedback.
• Document debug methodologies, technical findings, and solutions while contributing to internal knowledge bases and best practices.
• Support large-scale cluster bring-up activities, new product introductions, and strategic customer deployments as experience grows.
• Develop and deliver technical training sessions and collateral covering new products, feature enhancements, and advanced troubleshooting methodologies.

Requirements & Tech Stack

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

Ways To Stand Out

• Hands-on experience with modern data center GPU platforms, including AMD Instinct™ accelerators and comparable NVIDIA solutions.
• Direct experience with GPU programming frameworks such as ROCm HIP or CUDA.
• Experience using performance analysis and debugging tools such as rocProf, ROCgdb, AMDuProf, or PyTorch Profiler.
• Familiarity with high-performance networking technologies including InfiniBand, RoCE, and RDMA concepts.
• Knowledge of distributed GPU communication frameworks such as RCCL and NCCL.
• Experience with containerization and orchestration technologies such as Docker, Kubernetes, or Slurm.
• Experience supporting or debugging HPC clusters, AI training environments, inference deployments, or proof-of-concept systems.
• Previous experience working with OEMs, ODMs, cloud service providers, or large enterprise customers.