Senior GPU Performance Software Engineer
Senior GPU performance engineer on AMD's Datacenter Performance Group, analyzing AI workloads and doing pre-silicon SW/HW co-design to shape next-generation GPU architectures. Day-to-day involves performance analysis, benchmarking, and prototyping in C/C++ across the GPU software stack, including multi-GPU/distributed systems.
- Compensation
- Not disclosed
- Location
- US,TX,Austin
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
Nice to have
- Strong software development experience in C/C++ with a focus on performance-sensitive systems
- Strong understanding of computer architecture, performance analysis, and optimization techniques
- Experience with AMD, NVIDIA, or other GPU architectures
- Experience with GPU software stacks, including programming models, runtimes, drivers, firmware, or performance tools
- Experience with distributed and multi-GPU systems, including communication/synchronization technologies such as RCCL or MPI
- Experience using profiling, debugging, and tracing tools to analyze complex software and system performance issues
- Ability to leverage AI-assisted development tools, intelligent agents, and automation techniques to solve complex technical problems
- Familiarity with AI/ML workloads, GPU kernel development, or AI inference frameworks
- Excellent problem-solving, communication, and technical leadership skills
Full description
ADVANCE YOUR CAREER. ADVANCE THE WORLD.
At AMD, we believe technology 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 AMD is shaping the future.
Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.
THE ROLE
AMD's Datacenter Performance Group is seeking a GPU Performance Software Engineer to drive performance analysis, SW/HW co-design, and architecture evaluation for next-generation GPU-based AI systems.
In this role, you will work at the intersection of software, architecture, and performance engineering, analyzing AI workloads and system behavior to identify performance opportunities and influence future GPU designs. Working closely with architecture, runtime, driver, firmware, and AI software teams, you will evaluate new architectural concepts, prototype emerging hardware features, and guide optimization efforts across the GPU software stack.
The position is primarily focused on pre-silicon architecture evaluation and SW/HW co-design for next-generation AI inference systems, with opportunities to validate performance and investigate bottlenecks on emerging hardware platforms.
THE PERSON
The ideal candidate combines strong software engineering skills with a solid understanding of computer architecture and performance analysis.
You enjoy investigating complex performance problems, understanding interactions across large software stacks, and using data to influence future hardware and software designs. You are comfortable working across multiple layers of the GPU software stack and can effectively communicate technical findings to both software and hardware teams.
Experience with AMD, NVIDIA, or other GPU architectures is strongly preferred.
KEY RESPONSIBILITIES
- Analyze AI workloads on current and future GPU-based systems to identify performance bottlenecks and optimization opportunities across the software stack.
- Collaborate with architecture teams to evaluate, prototype, and influence next-generation GPU features through data-driven performance analysis and SW/HW co-design.
- Develop performance studies, benchmarks, and software prototypes to assess architectural tradeoffs and estimate the impact of future hardware capabilities.
- Investigate system-level performance across single-GPU, multi-GPU, and rack-scale environments, including communication, synchronization, memory, and workload distribution effects.