Sr. Silicon Design Engineer: Hardware-Software Co-Design
- Bellevue, WA, United States
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Original job description
ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believetechnology has the power to solve the world's most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMDis shapingthefuture. Whetheryou'redesigning next-gen processors, enabling AI breakthroughs, orbringing leading edge products to market, every role at AMD contributes to something bigger- technologythat moves the world forward.Join us and, together, we'll advance your career.
THE
ROLE:
The AMD AI Group is looking for a Senior Silicon Design Engineer to co-design the hardware-software interface for AMD Instinct GPUs. You will work with hardware architects to model and evaluate how GPU microarchitecture, AI frameworks, and the ROCm software stack interact, using that analysis to drive improvements in both performance and energy efficiency across the AI compute stack.
THE PERSON:
You have excellent communication and presentation skills, demonstrated through technical publications, presentations, trainings, executive briefings, etc. You are highly adept at collaboration among top-thinkers and engineers alike, ready to help elevate the knowledge and skills of the team around you.
KEY RESPONSIBILITIES:
Your day-to-day: build performance and power models that connect architectural decisions to real workload behavior. Evaluate proposed hardware features and software API designs against AI training and inference targets. Write and optimize GPU kernels and compiler passes that exploit microarchitectural characteristics. Work across the stack: the Linux kernel DRM/GEM layer, the ROCm runtime, the IREE compiler, and AI frameworks and DSLs like PyTorch and Triton. Hardware-Software Co-Design & Performance
- Leverage detailed performance models that bridge GPU microarchitecture and software execution, including instruction execution, memory hierarchy behavior, networking, and compute-memory overlap. You will use those models to drive both software optimization and hardware design feedback.
- Analyze and characterize AI workloads (training and inference) against AMD Instinct microarchitecture to identify performance bottlenecks, inform silicon roadmap decisions, and validate that architectural features deliver expected real-world speedups.
- Collaborate directly with AMD GPU hardware and firmware teams to evaluate proposed hardware features, providing software-grounded analysis of how changes to the memory subsystem, cache coherence protocols, or execution units affect workloads that matter.
- Own the feedback loop between workload characterization and hardware design: build the methodology, tooling, and benchmarks that make this loop fast and evidence-driven across Instinct generations.
Compiler & Runtime Infrastructure
- Contribute to the IREE compiler and ROCm stack, with focus on backend optimizations, runtime API extensions, and firmware command packet design that target AMD GPU microarchitecture.
- Design and optimize compute runtime paths including AI framework dispatch, data movement, and pipelining, minimizing overhead and maximizing hardware utilization for latency-sensitive and throughput-critical workloads.
- Work at the Linux kernel level on GPU compute infrastructure to improve asynchronous execution and reduce latency.
Cross-Cutting
- Contribute to the open ROCm ecosystem, across the stack, in ways that raises the bar for the entire AMD AI developer experience.
- Develop reproducible benchmarks and performance regression infrastructure that keeps the full stack honest across compiler, runtime, and driver changes.
- Represent the software perspective in silicon architecture reviews and represent hardware realities to the software and framework teams. Be the person who holds both models in their head at once.
- Collaborate with AMD architecture teams to provide software feedback on next-generation Instinct GPU designs for both training and inference workloads.
•
PREFERRED EXPERIENCE:
- Industry experience working at the boundary of GPU compute hardware and AI systems software.
- Strong computer architecture foundation: memory consistency and coherence models, GPU microarchitecture, pipelining, and the ability to reason precisely about what hardware is doing on a given workload.
- Demonstrated performance modeling skills and the ability to build analytical or simulation-based models that predict real hardware behavior and use them to make engineering decisions.
- Proficiency in C and C++ in system-level contexts, and Python
... For full info follow application link.AMD does not accept unsolicited resumes from headhunters, recruitment agencies or fee based recruitment services. AMD and its subsidiaries are equal opportunity employers. We consider candidates regardless of 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.