Lead AI Research Scientist, Hardware AI Systems

Advanced Micro Devices, Inc•Santa Clara, CA

3d•Hybrid

About The Position

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 a Lead AI Research Scientist, Hardware AI Systems, to develop AI systems that generalize across hardware engineering contexts, different SoC programs, toolchains, process nodes, and teams, without bespoke retraining for every program. You will work on metareasoning (when to call which tool or simulator), representation learning across flows (RTL, verification, physical design), and learning under high- or mixed-latency rewards typical of silicon signoff. You partner with methodology and silicon teams to turn one-off wins into reusable models, benchmarks, and transfer protocols that compound across AMD’s roadmap.

Requirements

Deep technical expertise and proven research experience in machine learning (ML), with emphasis on transfer learning, meta-learning, intelligent agents, and long-horizon reinforcement learning (RL).
Exposure to hardware development (RTL, verification, PD, or bring-up) or willingness to ramp with embedded experts
Experience with Large Language Models (LLM) tool use, planning, or hierarchical control in real toolchains
PhD in Computer Science, Machine Learning, or related field strongly preferred.

Responsibilities

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