AI Research Scientist, Hardware AI Systems

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.

THE ROLE:

We are hiring a 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.

THE PERSON:

You think in distribution shift, domain adaptation, and structured priors—not only bigger models. You respect domain experts and treat their time and signoff criteria as first-class constraints in problem formulation.

KEY 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
• PREFERRED EXPERIENCE: 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
• ACADEMIC CREDENTIALS: PhD in Computer Science, Machine Learning, or related field strongly preferred.

Responsibilities

THE ROLE:

We are hiring a 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.

THE PERSON:

You think in distribution shift, domain adaptation, and structured priors—not only bigger models. You respect domain experts and treat their time and signoff criteria as first-class constraints in problem formulation.

KEY 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
• PREFERRED EXPERIENCE: 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
• ACADEMIC CREDENTIALS: PhD in Computer Science, Machine Learning, or related field strongly preferred.

Qualifications