Lead AI Scientist, Hardware Systems & Generalization

AMD; Santa Clara (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

AMD benefits at a glance

Job summary

AMD in Santa Clara seeks a Lead AI Research Scientist, Hardware AI Systems to develop AI systems that generalize across hardware contexts, RTL, verification, PD and toolchains. You will partner with methodology and silicon teams to turn one-off wins into reusable models, benchmarks, and transfer protocols that compound across AMD’s roadmap.

PhD strongly preferred; deep ML expertise, transfer learning, meta-learning, and RL experience are essential.

Qualifications

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

Responsibilities

• Lead AI research initiatives spanning transfer learning, meta-learning, and RL for hardware AI systems.
• Define evaluation benchmarks, datasets, and translation protocols to reusable models across AMD programs.
• Collaborate with hardware teams to integrate ML methods into RTL, verification, and PD workflows.
• Mentor junior scientists and contribute to AMD technical standards and public research outputs.

Skills

Transfer learning

Meta-learning

Intelligent agents

Reinforcement learning

Machine learning research

LLM tooling

Education

PhD in Computer Science or Machine Learning

Tools

LLM tool use

Job description

AMD in Santa Clara seeks a Lead AI Research Scientist, Hardware AI Systems to develop AI systems that generalize across hardware contexts, RTL, verification, PD and toolchains. You will partner with methodology and silicon teams to turn one-off wins into reusable models, benchmarks, and transfer protocols that compound across AMD’s roadmap.

PhD strongly preferred; deep ML expertise, transfer learning, meta-learning, and RL experience are essential.