Director, AI Research - Recursive Self-Improvement

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:

AMD is looking for a Director of AI Research to lead AMD's recursive self-improvement (RSI) agenda: using AI—and reinforcement learning in particular—to make AMD's hardware design, kernel and compiler optimization, and engineering workflows measurably better, then feeding those gains back into the loop.

THE PERSON:

This is a deeply technical leadership role aimed squarely inside AMD. You will build and lead a research team, set the technical direction for the RSI flywheel, and partner across silicon, software, compiler, and engineering-productivity teams to turn research into compounding internal advantage. The ideal candidate is a recognized AI researcher who can go deep on RL training and systems and has strong opinions about what makes self-improving loops actually work (and where they break).

We are open to candidates who want to remain hands-on as player-coaches as well as those who lead primarily through their teams.

KEY RESPONSIBILITIES:

• Define and own AMD's internal recursive self-improvement research agenda—where AI improves AMD's hardware, kernels, compilers, and engineering workflows, and how those gains compound over time
• Build, lead, and mentor a high-caliber team of AI researchers and research engineers; set research direction, hiring bar, and standards for technical rigor
• Drive RL-based research on concrete internal targets—kernel and PPA optimization, design-space exploration, and code/workflow generation—grounded in measurable, verifiable improvement
• Design and own the verification and evaluation infrastructure (fast simulators, "slice" verifiers, benchmarks) needed to run self-improvement loops safely and trust their results
• Confront the hard failure modes of self-improving systems directly—reward hacking, evaluation gaming, and reward-signal scaling—and build research programs to detect and mitigate them
• Partner deeply with silicon, architecture, compiler, and engineering-productivity teams to embed research into real AMD design and development flows
• Set and track measurable goals for the program—verified performance gains, cycle-time reduction, and the rate at which improvements feed back into the loop
• Drive AMD's broader move toward AI-native engineering, helping rearchitect how internal teams build hardware and software around AI in the loop
• Represent the RSI research agenda to executive leadership; communicate progress, risks, and roadmap implications to both technical and business audiences
• Stay at the frontier of RL and AI-for-systems research and translate emerging techniques into internal opportunity
• PREFERRED EXPERIENCE: Demonstrated technical leadership in AI/ML research, with a track record of impactful published work and/or research shipped into production systems
• Deep, current expertise in reinforcement learning, including reward modeling, training pipelines, and the practical failure modes of RL at scale
• Hands-on understanding of AI-for-systems problems—code generation, compiler/kernel optimization, design-space exploration, or hardware/software co-design
• Experience building coding RL loops
• Experience leading research teams and setting technical direction across multiple concurrent programs
• Ability to remain hands-on with training pipelines, kernels, or research prototyping while leading a team
• Strong cross-functional collaboration skills, with a track record of embedding research into engineering organizations
• Track record of attracting, hiring, and developing top AI research talent
• ACADEMIC CREDENTIALS: PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent research experience and demonstrated impact

Responsibilities

THE ROLE:

AMD is looking for a Director of AI Research to lead AMD's recursive self-improvement (RSI) agenda: using AI—and reinforcement learning in particular—to make AMD's hardware design, kernel and compiler optimization, and engineering workflows measurably better, then feeding those gains back into the loop.

THE PERSON:

This is a deeply technical leadership role aimed squarely inside AMD. You will build and lead a research team, set the technical direction for the RSI flywheel, and partner across silicon, software, compiler, and engineering-productivity teams to turn research into compounding internal advantage. The ideal candidate is a recognized AI researcher who can go deep on RL training and systems and has strong opinions about what makes self-improving loops actually work (and where they break).

We are open to candidates who want to remain hands-on as player-coaches as well as those who lead primarily through their teams.

KEY RESPONSIBILITIES:

• Define and own AMD's internal recursive self-improvement research agenda—where AI improves AMD's hardware, kernels, compilers, and engineering workflows, and how those gains compound over time
• Build, lead, and mentor a high-caliber team of AI researchers and research engineers; set research direction, hiring bar, and standards for technical rigor
• Drive RL-based research on concrete internal targets—kernel and PPA optimization, design-space exploration, and code/workflow generation—grounded in measurable, verifiable improvement
• Design and own the verification and evaluation infrastructure (fast simulators, "slice" verifiers, benchmarks) needed to run self-improvement loops safely and trust their results
• Confront the hard failure modes of self-improving systems directly—reward hacking, evaluation gaming, and reward-signal scaling—and build research programs to detect and mitigate them
• Partner deeply with silicon, architecture, compiler, and engineering-productivity teams to embed research into real AMD design and development flows
• Set and track measurable goals for the program—verified performance gains, cycle-time reduction, and the rate at which improvements feed back into the loop
• Drive AMD's broader move toward AI-native engineering, helping rearchitect how internal teams build hardware and software around AI in the loop
• Represent the RSI research agenda to executive leadership; communicate progress, risks, and roadmap implications to both technical and business audiences
• Stay at the frontier of RL and AI-for-systems research and translate emerging techniques into internal opportunity
• PREFERRED EXPERIENCE: Demonstrated technical leadership in AI/ML research, with a track record of impactful published work and/or research shipped into production systems
• Deep, current expertise in reinforcement learning, including reward modeling, training pipelines, and the practical failure modes of RL at scale
• Hands-on understanding of AI-for-systems problems—code generation, compiler/kernel optimization, design-space exploration, or hardware/software co-design
• Experience building coding RL loops
• Experience leading research teams and setting technical direction across multiple concurrent programs
• Ability to remain hands-on with training pipelines, kernels, or research prototyping while leading a team
• Strong cross-functional collaboration skills, with a track record of embedding research into engineering organizations
• Track record of attracting, hiring, and developing top AI research talent
• ACADEMIC CREDENTIALS: PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent research experience and demonstrated impact

Qualifications