Senior AI/ML and GPU Performance QA engineer

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AI/ML and GPU Performance QA engineer

We are seeking an experienced Senior Technical Validation Engineer to drive validation and performance engineering for Machine Learning (ML), High-Performance Computing (HPC) frameworks, GPU software stacks, and cluster environments.

This role requires good understanding and experience in ROCm, CUDA, GPU architecture, ML frameworks, CI/CD systems, benchmarking, and competitive analysis.
You will lead cross-functional initiatives across validation, automation, test development, performance tuning, and system scalability, ensuring delivery of high-quality, high-performance software for next-generation AI and HPC workloads.

Key Responsibilities

• Lead validation for ML/AI models: accuracy testing, performance benchmarking, regression, drift detection, A/B testing
• Test ML frameworks: PyTorch, Hugging Face, MLFlow experiment tracking
• Validate wide varieties of AI models to ensure correctness in distributed training or inference
• Perform GPU testing & profiling: ROCm/CUDA validation, performance profiling, memory/thermal analysis, multi-GPU scaling
• Validate HPC frameworks, distributed runtimes, compilers, and GPU libraries
• Build scalable CI/CD workflows for ML/HPC validation. Develop automated test pipelines using Docker, Kubernetes, GitHub Actions, Jenkins
• Validate cloud-based AI workloads on AWS SageMaker, Lambda, and S3
• Test the benchmarks under containerized and virtualized GPU environments
• Design and implement automated validation pipelines for ML frameworks (e.g., PyTorch, TensorFlow, JAX) across GPU platforms.
• Develop and maintain benchmarking suites for AI models and HPC workloads, focusing on performance, scalability, and regression detection.
• Multi-node validation efforts using orchestration tools (e.g., Slurm, MPI, Kubernetes) to simulate real-world distributed training and inference.
• Collaborate with hardware and software teams to validate GPU hardware platforms (NVIDIA CUDA, AMD ROCm) for ML and HPC readiness.
• Analyze performance metrics using profiling tools (e.g., Nsight, rocprof, perf) and provide actionable insights.
• Drive test content development for emerging AI workloads, including LLMs, vision models, and scientific computing benchmarks.
• Perform bottleneck analysis, hyperparameter validation, and competitive benchmarking
• Mentor junior engineers and contribute to validation strategy, tooling, and best practices.

Preferred Expereince :

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
• 8+ years of experience in validation engineering, ML infrastructure, or HPC performance testing.
• Strong hands-on experience with GPU platforms (NVIDIA CUDA, AMD ROCm) and their software ecosystems.
• Deep understanding of AI model architectures, training/inference workflows, and ML performance bottlenecks.
• Proven experience with CI/CD systems, Git, Docker, and automated test frameworks.
• Expertise in multi-node orchestration and distributed system validation.
• Familiarity with HPC benchmarks (e.g., HPL, HPCG, MLPerf) and AI model benchmarking methodologies.
• Proficiency in scripting and automation (Python, Bash, YAML) in Linux environments.
• Strong communication, documentation, and cross-functional collaboration skills.

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Benefits offered are described: .

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.