AMD
From AMD's careers page
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THE ROLE: As a core member of the team, you will play a pivotal role in optimizing and developing deep learning frameworks for AMD GPUs.
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.
As a core member of the team, you will play a pivotal role in optimizing and developing deep learning frameworks for AMD GPUs. Your experience will be critical in enhancing GPU kernels, deep learning models, and training/inference performance across multi-GPU and multi-node systems. You will engage with both internal GPU library teams and open-source maintainers to ensure seamless integration of optimizations, utilizing cutting-edge compiler technologies and advanced engineering principles to drive continuous improvement.
Skilled engineer with strong technical and analytical expertise in C++ development within Linux environments. The ideal candidate will thrive in both collaborative team settings and independent work, with the ability to define goals, manage development efforts, and deliver high-quality solutions. Strong problem-solving skills, a proactive approach, and a keen understanding of software engineering best practices are essential.
End to end optimization: Build and optimize end to end distributed inference (e.g, P/D disaggregation and Large-EP) and RL solutions on mainstream frameworks like vLLM and SGlang.
Collaborate with GPU Library Teams: Work closely with internal teams to analyze and improve training and inference performance on AMD GPUs.
Collaborate with Open-Source Maintainers: Engage with framework maintainers to ensure code changes are aligned with requirements and integrated upstream.
Work in Distributed Computing Environments: Optimize deep learning performance on both scale-up (multi-GPU) and scale-out (multi-node) systems.
Utilize Cutting-Edge Compiler Tech: Leverage advanced compiler technologies to improve deep learning performance.
Optimize Deep Learning Pipeline: Enhance the full pipeline, including integrating graph compilers.
Software Engineering Best Practices: Apply sound engineering principles to ensure robust, maintainable solutions.
GPU Kernel Development & Optimization: Deep experienced in designing and optimizing GPU kernels for deep learning on AMD GPUs using HIP, CUDA, and assembly (ASM). Strong knowledge of AMD architectures (GCN, RDNA) and low-level programming to maximize performance for AI operations, leveraging tools like Compute Kernel (CK), CUTLASS, and Triton for multi-GPU and multi-platform performance.
Deep Learning Integration: Strong experienced in integrating optimized GPU performance into machine learning and LLM frameworks (e.g., vLLM, SGlang,TensorFlow, PyTorch) to accelerate model training and inference, with a focus on scaling and throughput.
End to end solution optimization: Understand the latest market trend of LLM and multimodal, solid hands-on E2E performance tuning experience on distributed inference (e.g, P/D disaggregation and Large-EP) and RL. Experienced in Text to Video or Image to Video is a plus.
Software Engineering: Skilled in Python and C++, with experience in debugging, performance tuning, and test design to ensure high-quality, maintainable software solutions.
High-Performance Computing: Expert experienced in running large-scale workloads on heterogeneous computing clusters, optimizing efficiency and scalability.
Compiler Optimization: Solid understanding of compiler theory and tools like LLVM and ROCm for kernel and system performance optimization.
Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related fields.
5+ years of professional experience in technical software development, with a focus on GPU optimization, performance engineering, and framework development.
#LI-FL1
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.
As a core member of the team, you will play a pivotal role in optimizing and developing deep learning frameworks for AMD GPUs. Your experience will be critical in enhancing GPU kernels, deep learning models, and training/inference performance across multi-GPU and multi-node systems. You will engage with both internal GPU library teams and open-source maintainers to ensure seamless integration of optimizations, utilizing cutting-edge compiler technologies and advanced engineering principles to drive continuous improvement.
Skilled engineer with strong technical and analytical expertise in C++ development within Linux environments. The ideal candidate will thrive in both collaborative team settings and independent work, with the ability to define goals, manage development efforts, and deliver high-quality solutions. Strong problem-solving skills, a proactive approach, and a keen understanding of software engineering best practices are essential.
End to end optimization: Build and optimize end to end distributed inference (e.g, P/D disaggregation and Large-EP) and RL solutions on mainstream frameworks like vLLM and SGlang.
Collaborate with GPU Library Teams: Work closely with internal teams to analyze and improve training and inference performance on AMD GPUs.
Collaborate with Open-Source Maintainers: Engage with framework maintainers to ensure code changes are aligned with requirements and integrated upstream.
Work in Distributed Computing Environments: Optimize deep learning performance on both scale-up (multi-GPU) and scale-out (multi-node) systems.
Utilize Cutting-Edge Compiler Tech: Leverage advanced compiler technologies to improve deep learning performance.
Optimize Deep Learning Pipeline: Enhance the full pipeline, including integrating graph compilers.
Software Engineering Best Practices: Apply sound engineering principles to ensure robust, maintainable solutions.
GPU Kernel Development & Optimization: Deep experienced in designing and optimizing GPU kernels for deep learning on AMD GPUs using HIP, CUDA, and assembly (ASM). Strong knowledge of AMD architectures (GCN, RDNA) and low-level programming to maximize performance for AI operations, leveraging tools like Compute Kernel (CK), CUTLASS, and Triton for multi-GPU and multi-platform performance.
Deep Learning Integration: Strong experienced in integrating optimized GPU performance into machine learning and LLM frameworks (e.g., vLLM, SGlang,TensorFlow, PyTorch) to accelerate model training and inference, with a focus on scaling and throughput.
End to end solution optimization: Understand the latest market trend of LLM and multimodal, solid hands-on E2E performance tuning experience on distributed inference (e.g, P/D disaggregation and Large-EP) and RL. Experienced in Text to Video or Image to Video is a plus.
Software Engineering: Skilled in Python and C++, with experience in debugging, performance tuning, and test design to ensure high-quality, maintainable software solutions.
High-Performance Computing: Expert experienced in running large-scale workloads on heterogeneous computing clusters, optimizing efficiency and scalability.
Compiler Optimization: Solid understanding of compiler theory and tools like LLVM and ROCm for kernel and system performance optimization.
Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related fields.
5+ years of professional experience in technical software development, with a focus on GPU optimization, performance engineering, and framework development.
#LI-FL1
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.
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