The Lead AI Research Scientist, Hardware AI Systems at AMD will focus on developing AI systems that generalize across various hardware engineering contexts and teams. This role emphasizes metareasoning, representation learning, and adapting to high-latency rewards typical in silicon signoff. The successful candidate will collaborate with methodology and silicon teams to create reusable models and benchmarks. The position requires a strong understanding of machine learning and respect for domain experts in problem formulation.

• PhD in Computer Science, Machine Learning, or a related field is strongly preferred.
• Deep technical expertise and proven research experience in machine learning, particularly in transfer learning, meta-learning, and reinforcement learning.
• Exposure to hardware development such as RTL, verification, or physical design, or a willingness to learn from embedded experts.
• Experience with Large Language Models tool use, planning, or hierarchical control in real toolchains.

• Research transfer and multi-task learning for engineering agents across IP blocks, tools, and program generations.
• Develop metareasoning policies for budgeting expensive EDA steps and recovering from tool or data failures.
• Build cross-program benchmarks and datasets that expose generalization gaps and track progress over time.
• Collaborate with reinforcement learning scientists on reward shaping and credit assignment when feedback is slow or heterogeneous.
• Publish research at top venues and maintain internal technical standards for generalization claims backed by evidence.

• AMD provides a competitive 'Total Rewards' package that focuses on financial growth, health, and work-life balance.