Job description

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 Applied AI Research to lead research that sits where AI, compute, and AMD's hardware meet. You will lead applied research that uses AI to advance AMD's compute and hardware, with agents, reinforcement learning, and recursive self-improvement (RSI) as central tools for making those gains compound. You will collaborate with our AI research teams and hardware engineering teams to make this happen. THE PERSON: You will build and lead a team of applied researchers and research engineers, set technical direction across compute- and hardware-focused workstreams, and own the path from promising idea to validated, adopted capability. The ideal candidate is a researcher who is energized by real systems and real constraints. It’s someone who can frame a research question, run the experiment, build the verifier that proves it worked, and partner with different engineering teams to land it. 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 an applied AI research agenda focused on compute and hardware—kernel and compiler optimization, PPA and design-space exploration, and AI-for-systems, on the highest priority engineering projects at the company • Build, lead, and mentor a high-caliber team of applied researchers and research engineers; set research direction, hiring bar, and standards for rigor and delivery • Use recursive self-improvement loops to drive compounding, verifiable gains on concrete compute and hardware targets • Design and partner with teams to own the evaluation, simulation, and verification infrastructure (fast verifiers, benchmarks) that makes applied results trustworthy and ready to adopt • Confront the practical failure modes of RL and self-improving loops—reward hacking, evaluation gaming, reward-signal scaling—and build research and/or partner with research teams to detect and mitigate them • Own the full path from research to impact: frame the problem, run the experiments, validate the result, and partner with engineering to land it in real flows • Partner deeply across silicon, architecture, compiler, software, and AI-for-hardware engineering teams to ground research in real problems and ensure adoption • Set and track measurable outcomes—verified performance and PPA gains, cycle-time reduction, and the rate at which improvements feed back into the loop • Represent the applied research agenda to executive leadership; communicate progress, trade-offs, and roadmap implications to technical and business audiences • Stay at the frontier of RL and AI-for-systems research and translate emerging techniques into applied opportunity for AMD PREFERRED EXPERIENCE: • Demonstrated technical leadership in applied AI/ML research, with a track record of research that reached real systems or production impact • Deep, current expertise in reinforcement learning, including reward modeling, training pipelines, and the practical failure modes of RL at scale • Strong grounding in AI-for-systems and compute/hardware problems—kernel or compiler optimization, design-space exploration, or hardware/software co-design • Experience building the evaluation, simulation, or verification infrastructure that applied research and training loops depend on • Experience leading research teams and setting direction across multiple concurrent workstreams • 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 landing research inside engineering organizations • Track record of attracting, hiring, and developing top applied AI research talent ACADEMIC CREDENTIALS: • PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent research experience and demonstrated impact LOCATION: Santa Clara, CA #LI-BW1 Benefits offered are described: AMD benefits at a glance . AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Requirements

Must-haves

Build, lead, and mentor a high-caliber team of applied researchers and research engineers; set research direction, hiring bar, and standards for rigor and delivery
Use recursive self-improvement loops to drive compounding, verifiable gains on concrete compute and hardware targets
Design and partner with teams to own the evaluation, simulation, and verification infrastructure (fast verifiers, benchmarks) that makes applied results trustworthy and ready to adopt
Confront the practical failure modes of RL and self-improving loops—reward hacking, evaluation gaming, reward-signal scaling—and build research and/or partner with research teams to detect and mitigate them
Own the full path from research to impact: frame the problem, run the experiments, validate the result, and partner with engineering to land it in real flows
Partner deeply across silicon, architecture, compiler, software, and AI-for-hardware engineering teams to ground research in real problems and ensure adoption
Set and track measurable outcomes—verified performance and PPA gains, cycle-time reduction, and the rate at which improvements feed back into the loop
Represent the applied research agenda to executive leadership; communicate progress, trade-offs, and roadmap implications to technical and business audiences
Stay at the frontier of RL and AI-for-systems research and translate emerging techniques into applied opportunity for AMD
PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent research experience and demonstrated impact
Santa Clara, CA
Benefits offered are described: AMD benefits at a glance .
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.

Nice-to-haves

Demonstrated technical leadership in applied AI/ML research, with a track record of research that reached real systems or production impact
Deep, current expertise in reinforcement learning, including reward modeling, training pipelines, and the practical failure modes of RL at scale
Strong grounding in AI-for-systems and compute/hardware problems—kernel or compiler optimization, design-space exploration, or hardware/software co-design
Experience building the evaluation, simulation, or verification infrastructure that applied research and training loops depend on
Experience leading research teams and setting direction across multiple concurrent workstreams
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 landing research inside engineering organizations
Track record of attracting, hiring, and developing top applied AI research talent

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learning

Platform: amd
Effort: medium