KEY RESPONSIBILITIES
Develop & Test Features: Implement, test and debug features in collective communication libraries and network software, under the guidance of senior engineers.
Profile & Analyze Workloads: Profile LLM, diffusion, multimodal, RecSys and generative AI workloads on single-node and multi-node systems, and help identify performance bottlenecks.
Run Benchmarks & Experiments: Run performance experiments across AMD GPU generations and network configurations, and summarize results for the team.
Contribute to Tooling: Contribute to tools and infrastructure for performance measurement, regression tracking and reporting.
Support Model & Framework Enablement: Support new model enablement and out-of-box performance efforts across AI frameworks, and work with internal teams and open-source communities to get changes reviewed and integrated.
Software Engineering Best Practices: Follow sound engineering practices, including code reviews, testing and documentation, to deliver robust, maintainable code.
Learn & Grow: Build expertise in collectives, networking and GPU performance through mentorship, code reviews, knowledge sharing and hands-on project work.
PREFERRED EXPERIENCE
Collective Communication & Network Fundamentals: Understanding of collective communication concepts (e.g., all-reduce, all-to-all), and familiarity with libraries such as RCCL, NCCL or MPI and with networking fundamentals such as RDMA, NICs and interconnects.
GPU Programming & Architecture: Foundational knowledge of GPU architecture; experience with HIP, CUDA or similar GPU programming is a plus.
Deep Learning Frameworks: Familiarity with AI frameworks such as PyTorch, JAX, vLLM or SGLang and with distributed training or inference concepts.
Software Engineering Fundamentals: Proficiency in C++ and Python, with experience in debugging, profiling and writing tests.
Problem Solving & Communication: Strong problem-solving skills, eagerness to learn, and good written and verbal communication skills.
Industry Experience: professional experience in software development, with exposure to performance engineering, systems software, networking, or GPU programming.
ACADEMIC CREDENTIALS
Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
LOCATION
San Jose, CA or Austin, TX - onsite.
This role is not eligible for visa sponsorship.
#LI-MV1
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
As a member of the Collectives and Network Performance team, you will contribute to developing, testing and optimizing collective communication and networking software for AMD GPUs. You will help improve training and inference performance across multi-GPU and multi-node systems, working with senior engineers and with internal GPU library, runtime and ROCm teams, and learning how to integrate optimizations across AMD GPU generations and a range of AI models.
THE PERSON
Seeking a motivated C++ developer with a solid foundation in Linux environments, computer architecture and networking concepts. The ideal candidate completes well-defined tasks with guidance, learns quickly, and collaborates effectively within a high-performing team. You are comfortable profiling and debugging performance issues and are interested in how AI model architectures map onto software and hardware. Familiarity with GPU programming and distributed training or inference is a plus.
KEY RESPONSIBILITIES
Develop & Test Features: Implement, test and debug features in collective communication libraries and network software, under the guidance of senior engineers.
Profile & Analyze Workloads: Profile LLM, diffusion, multimodal, RecSys and generative AI workloads on single-node and multi-node systems, and help identify performance bottlenecks.
Run Benchmarks & Experiments: Run performance experiments across AMD GPU generations and network configurations, and summarize results for the team.
Contribute to Tooling: Contribute to tools and infrastructure for performance measurement, regression tracking and reporting.
Support Model & Framework Enablement: Support new model enablement and out-of-box performance efforts across AI frameworks, and work with internal teams and open-source communities to get changes reviewed and integrated.
Software Engineering Best Practices: Follow sound engineering practices, including code reviews, testing and documentation, to deliver robust, maintainable code.
Learn & Grow: Build expertise in collectives, networking and GPU performance through mentorship, code reviews, knowledge sharing and hands-on project work.
PREFERRED EXPERIENCE
Collective Communication & Network Fundamentals: Understanding of collective communication concepts (e.g., all-reduce, all-to-all), and familiarity with libraries such as RCCL, NCCL or MPI and with networking fundamentals such as RDMA, NICs and interconnects.
GPU Programming & Architecture: Foundational knowledge of GPU architecture; experience with HIP, CUDA or similar GPU programming is a plus.
Deep Learning Frameworks: Familiarity with AI frameworks such as PyTorch, JAX, vLLM or SGLang and with distributed training or inference concepts.
Software Engineering Fundamentals: Proficiency in C++ and Python, with experience in debugging, profiling and writing tests.
Problem Solving & Communication: Strong problem-solving skills, eagerness to learn, and good written and verbal communication skills.
Industry Experience: professional experience in software development, with exposure to performance engineering, systems software, networking, or GPU programming.
ACADEMIC CREDENTIALS
Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
LOCATION
San Jose, CA or Austin, TX - onsite.
This role is not eligible for visa sponsorship.
#LI-MV1
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.
