Senior Field Application Engineer – AI - TX, Austin, United States

Originally posted 3 April 2026 by the employer. Relisted by the employer on 23 July 2026.

AMD Instinct datacenter GPUs, AMD Pensando Pollara AI NICs, and EPYC CPUs

About the role

AMD is seeking a Field Application Engineer to join the AI and HPC Center of Excellence to support RFP-driven customer requests and enable AI workloads on AMD Instinct datacenter GPUs.

What you'll do

- Enable customers to execute AI workloads on AMD Instinct GPUs, AMD Pensando Pollara AI NICs, and EPYC CPUs, and test requested workloads for RFP responses. - Build technical relationships with customer engineers and architects through application- and system/MLOps-focused POCs, presentations, and training. - Execute AI inference and training workloads, evaluate performance across system and software configurations, and benchmark against competitive hardware. - Conduct performance investigations using PyTorch, TensorFlow, and JAX alongside MLperf and Hugging Face benchmarks. - Author internal and external technical collateral, including tuning guides, whitepapers, and training materials. - Collaborate across AMD GPU Business Unit, Engineering, Architecture, and Software teams to submit JIRA tickets, resolve partner technical issues, and provide field requirement feedback.

What you'll need

- Professional track record undertaking AI inference or training as a key functional responsibility. - Demonstrable hands-on expertise working with popular AI frameworks. - Experience delivering presentations and technical training both remotely and in person. - Ability to travel domestically and internationally approximately 10-20% over a year.

Nice to have

- Demonstrated experience with training and inference workloads on GPUs. - Experience executing applications in PyTorch, TensorFlow, or JAX, and using AI repositories such as Hugging Face and MLPerf. - MLOps experience with KVM, Kubernetes, OpenStack, or OpenShift. - Understanding of how frameworks, precision, compilers, libraries, middleware, and system-level hardware design impact performance. - Programming experience with HIP, CUDA, Python, C/C++, Fortran, OpenACC, OpenMP, or pSTL. - Understanding of memory and cache hierarchy, register-level dataflow, or inter-node network choices at scale. - Linux administration experience for HPC/AI middleware setup, datacenter deployment, or active government level security clearance.

Skills: AI workloads, data center GPUs, RFP, training, inference, AI frameworks