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Software Research & Development Engineer – GPGPU Kernel Development

Key Responsibilities

Develop GPU Kernels: Create and optimize GPU kernels to maximize performance for specific ML/AI/HPC workloads on pre-silicon architectures.
Develop & Optimize Models: Design and optimize low-level GPU kernels to accelerate inference and training of large machine learning models. Maximize computational efficiency and reduce execution time while ensuring model accuracy.
Multi-GPU and Multi-Node Optimization: Design and implement strategies for distributed model training and inference across multiple GPUs and nodes. Address data parallelism and model parallelism challenges to fully utilize available resources.
Performance Profiling: Profile and analyze system and application performance to identify bottlenecks and areas for improvement. Use profiling tools to understand and optimize hardware resource utilization.
Parallel Computing: Leverage parallel computing techniques to improve the scalability and performance of machine learning workloads. Implement multi-threading and GPU synchronization techniques.
Benchmarking and Testing: Develop benchmarks and testing procedures to assess the performance and stability of optimized models and frameworks. Ensure that the solutions meet or exceed the defined performance criteria.
ML Frameworks: Use, enhance and optimize frameworks like PyTorch, vLLM, SGLang for AMD GPUs in open-source repositories.
Collaborate with GPU Library Teams: Work closely with internal teams to analyze and improve workloads performance on AMD GPUs and pre-silicon architectures.
Software Engineering Best Practices: Apply sound engineering principles to ensure robust, maintainable solutions.
Documentation: Create detailed documentation of optimizations, best practices, and implementation guidelines to facilitate knowledge sharing and maintainable code.

Required Experience

Proficient in C++ programming, C++17 and/or C++20.
Experienced in designing and optimizing GPU kernels on AMD/Nvidia GPUs using HIP, CUDA, and/or assembly (ASM).
Experienced in low-level programming to maximize performance for AI operations, leveraging tools like Compute Kernel (CK) and/or CUTLASS for multi-GPU and multi-platform performance.
Experience using profiling and benchmark tooling for large models.
Excellent problem-solving skills and attention to detail.
Strong communication and teamwork skills.

Preferred Experience

Experience in C++23/26.
Experience with model optimization, such as low-precision quantization (MXFP4, FP8, INT4).
Experience with AI software frameworks, such as PyTorch, vLLM, SGLang, etc.
Knowledge of model architectures, LLMs, MoE, and diffusion.
Knowledge of AMD architectures (GCN, RDNA, CDNA)
Experience in running large-scale HPC workloads on heterogeneous compute clusters, optimizing for efficiency and scalability.
Knowledge of compiler theory and tools like LLVM and ROCm for kernel and system performance optimization.

Academic Credentials

PhD in Computer Science, Computational Science, Computer Engineering, Electrical Engineering, or a related field.

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.