Applied AI Engineer, Top Hardware Priorities

Advanced Micro Devices, Inc•Santa Clara, CA

72d•Hybrid

About The Position

At AMD, we are building great products that accelerate next-generation computing experiences. We are hiring Applied AI Engineers to work directly with hardware and software engineering teams on high-priority AI-for-engineering efforts. This role involves transforming 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 collaborate 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.

Requirements

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 workflows, or hardware-adjacent tooling.
Excellent collaboration and communication skills with domain experts, engineering leads, research teams, and program stakeholders.
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience.

Nice To Haves

Experience with hardware design or verification workflows, including RTL, Verilog/SystemVerilog, simulation, formal verification, EDA tools, timing analysis, or power/performance/area tradeoffs.
Experience with GPU/CPU performance engineering, compiler tooling, profilers, kernel optimization, ROCm/HIP, CUDA, or benchmarking.
Experience designing evaluation datasets, graders, dashboards, leaderboards, or experiment tracking systems.
Familiarity with LLM agents, tool use, retrieval, LLM-as-judge workflows, RL, or post-training methods.
Experience working with internal engineering customers, forward-deployed teams, or cross-functional product engineering programs.
Master's degree preferred; PhD is a plus for candidates with relevant AI, systems, EDA, or hardware/software co-design experience.

Responsibilities

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.