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AMD is looking for a skilled and motivated software engineer to join the Model Automation and Dashboarding (Framework MAD) team — a group focused on ensuring the reliability, performance, and scalability of AI models running on AMD hardware. As part of this team, you’ll build and maintain tools and infrastructure that automate functional and performance validation of deep learning models across ROCm and GPU platforms. Your contributions will directly impact developer confidence, model portability, and transparent benchmarking for internal teams and the open-source community.
Job Responsibility:
AI Model Enablement & Optimization: Enable and optimize key AI models (LLM, Vision, MultiModal, etc.) on AMD GPUs. Optimize AI frameworks like PyTorch, TensorFlow, etc., on AMD GPUs in upstream open-source repositories
Collaboration & Integration: Collaborate with internal GPU library teams and open-source framework maintainers to analyze, optimize, and integrate code changes upstream
Model Testing & Validation: Build and maintain automated functional and performance testing pipelines for AI models across ROCm-supported hardware using scalable tools
Benchmarking & Metrics: Develop tools and automation for continuous benchmarking and regression tracking across hardware generations and ROCm releases. Build and maintain real-time dashboards that report relevant performance, accuracy, and reliability metrics
Ecosystem & Open-Source Contributions: Support public-facing MAD GitHub repositories and Docker releases, enabling the community to run and validate models on ROCm. Contribute to the design of portable, easy-to-use Python interfaces that support multi-node profiling, distributed workloads, and containerized deployments
Requirements:
Undergraduate and/or Master’s Degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
Strong C/C++/Python programming and software design skills, including debugging, performance analysis, and test design
Experience in test automation, CI/CD, and Linux scripting
Knowledge of GPU computing (HIP, CUDA, OpenCL)
AI model experience or knowledge in Natural Language Processing, Vision, Audio, Recommendation systems
Knowledge of Docker, Kubernetes, or Ansible for testing and deploying AI models and services at scale
Experience with profiling tools, system monitoring, or regression tracking systems for deep learning models
Proficiency with version control (GitHub), testing strategies, code reviews, and collaborative software development
Strong written and verbal communication skills with a proactive approach to defining and driving development efforts