Applied AI Engineer, Top Hardware Priorities

WHAT YOU DO AT AMD CHANGES EVERYTHING   At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.   Together, we advance your career.

We are hiring Applied AI Engineers to work directly with hardware and software engineering teams on high-priority AI-for-engineering efforts. This role turns difficult engineering workflows into AI-assisted systems that generate candidates, validate correctness, measure quality, and help engineers move faster on real product problems. You will work across hardware-adjacent domains such as design optimization, verification, simulation, firmware, performance debugging, routing, issue triage, and compute library optimization. The work is practical, measured, and deeply collaborative with domain experts.   THE PERSON You are a builder who likes ambiguous, high-impact engineering problems. You can sit with domain experts, understand the real workflow, identify what can be measured, build the tool or agent loop, and iterate until it saves engineering time or improves a concrete metric. You value correctness, validation, and workflow fit as much as model capability.

Build applied AI workflows for top hardware and software engineering priorities, including optimization, verification, debugging, simulation, and automation workflows. Convert manual engineering processes into structured tasks with inputs, candidate generation, validation, scoring, logging, and reproducible comparisons. Partner with domain owners to define success metrics such as correctness, performance, resource usage, quality, latency, coverage, engineer time saved, or issue classification accuracy. Develop tools that let AI agents use compilers, simulators, formal checks, profilers, benchmark harnesses, ticket systems, and engineering knowledge sources. Build human-in-the-loop workflows for tasks where annotated data, expert judgment, or subjective triage is required. Improve model and agent performance through better prompts, tool interfaces, retrieval, evaluation datasets, graders, and structured feedback. Work with research and infrastructure teams to generalize repeated patterns into reusable platforms, harnesses, dashboards, and data assets. Communicate progress through measurable outcomes, demos, concise technical writeups, and clear stakeholder updates. TECHNICAL FOCUS AREAS AI-assisted hardware design and software optimization workflows. Automated validation using tests, benchmarks, formal checks, simulators, and domain-specific graders. Agentic systems for code generation, debugging, root-cause analysis, issue triage, and engineering workflow automation. Evaluation design for tasks where ground truth may be sparse, expensive, or partially subjective. Tooling that connects LLMs and agents to real engineering systems while preserving reliability and auditability. Collaboration with domain experts to turn expert workflows into repeatable AI-assisted processes.

Strong software engineering experience in Python and at least one systems language such as C++, C, HIP, CUDA, or Rust. Experience building applied AI, ML, agentic, automation, or developer tooling systems for technical users. Ability to work with complex engineering tools, logs, tests, benchmark harnesses, and validation workflows. Strong debugging and root-cause analysis skills across software systems, AI