AI Solutions Architect / Field Application Engineer (FAE)

Austin, TX

Full-time

Hybrid

New Grad, Entry Level

AMD is a company dedicated to building innovative products that enhance computing experiences across various domains. They are seeking an Entry-Level AI Solutions Architect / Field Application Engineer to engage with customers and internal teams to deploy and optimize AI workloads on AMD platforms.

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Responsibilities

Serve as a technical point of contact for customers, supporting AI and HPC workloads on AMD CPU and GPU platforms
Work directly with customers to understand their use cases, requirements, and constraints, and guide them through solution design and deployment
Deliver technical presentations, demos, and architecture walkthroughs to both technical and non-technical audiences
Program-manage customer opportunities as they grow in complexity, coordinating activities across internal and external stakeholders
Perform hands-on system bring-up including hardware installation, firmware configuration, OS installation, and driver setup
Deploy and validate open-source AI and HPC software stacks (e.g., Linux, ROCm, AI frameworks, containers)
Run functionality, performance, and scalability benchmarks on CPU and GPU workloads
Perform first-level profiling and analysis of applications to identify performance bottlenecks and optimization opportunities
Support AI workloads such as training, inference, and data preprocessing across CPU and GPU platforms
Develop working knowledge of AMD CPU and GPU architectures and how they impact real-world workloads
Understand full-stack solutions spanning hardware, system software, drivers, frameworks, and applications
Assist in solution design for on-premises, cloud, and hybrid deployments
Collaborate closely with engineering, product management, marketing, and sales teams to represent customer needs
Provide structured feedback from the field to help influence product features, documentation, and roadmap decisions
Contribute to internal knowledge sharing, best practices, and team initiatives

Qualification

Required

Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent practical experience)
Strong interest in AI/ML technologies and a desire to work across hardware and software layers
Hands-on experience with Linux-based systems
Programming experience in one or more of the following: Python, C/C++, Bash
Familiarity with AI frameworks or tools (e.g., PyTorch, TensorFlow, ONNX, Hugging Face, or similar)
Strong communication skills with the ability to explain technical concepts clearly
Ability to work effectively in a team-oriented, cross-functional environment

Preferred

Experience working with GPU computing and/or accelerator-based workloads
Exposure to profiling and performance analysis tools for CPU and GPU workloads
Understanding of computer architecture concepts (CPU pipelines, memory hierarchy, GPU execution models)
Experience setting up or working in a lab environment with servers, networking, and storage
Familiarity with cloud platforms such as AWS, Azure, Google Cloud, or Oracle Cloud, including compute instance types and accelerators
Knowledge of containers and orchestration (Docker, Kubernetes)
Prior internship, co-op, or project experience in customer-facing or field engineering roles