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.
Participate in pre-silicon architecture evaluation and post-silicon performance validation, driving improvements across hardware and software teams.
PREFERRED QUALIFICATIONS
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 GPU architectures and GPU software stacks, including programming models, runtimes, drivers, firmware, or performance tools.
Experience with distributed and multi-GPU systems, including communication and synchronization technologies such as RCCL, MPI, or similar frameworks.
Experience using profiling, debugging, and tracing tools to analyze complex software and system performance issues.
Demonstrated ability to leverage AI-assisted development tools, intelligent agents, and automation techniques to solve complex technical problems, accelerate analysis, and improve engineering effectiveness.
Familiarity with AI/ML workloads, GPU kernel development, or AI inference frameworks is a plus.
Excellent problem-solving, communication, and technical leadership skills.
ACADEMIC CREDENTIALS
Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.
This role is not eligible for visa sponsorship.
#LI-RL1
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.
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.
Participate in pre-silicon architecture evaluation and post-silicon performance validation, driving improvements across hardware and software teams.
PREFERRED QUALIFICATIONS
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 GPU architectures and GPU software stacks, including programming models, runtimes, drivers, firmware, or performance tools.
Experience with distributed and multi-GPU systems, including communication and synchronization technologies such as RCCL, MPI, or similar frameworks.
Experience using profiling, debugging, and tracing tools to analyze complex software and system performance issues.
Demonstrated ability to leverage AI-assisted development tools, intelligent agents, and automation techniques to solve complex technical problems, accelerate analysis, and improve engineering effectiveness.
Familiarity with AI/ML workloads, GPU kernel development, or AI inference frameworks is a plus.
Excellent problem-solving, communication, and technical leadership skills.
ACADEMIC CREDENTIALS
Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.
This role is not eligible for visa sponsorship.
#LI-RL1
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.
