About the job
We are seeking a highly experienced AI Software Architect to join our CPU Diagnostics team. This role is central to embedding machine learning into the design and development of CPU diagnostic and test solutions. You will lead the creation of autonomous AI-driven systems for test execution, results analysis, and failure debugging, significantly improving the speed, accuracy, and effectiveness of CPU diagnostics across the product lifecycle
Responsibilities
Architect, design, and implement machine learning models and algorithms for CPU functional testing, manufacturing testing, and diagnostic applications.
Lead the integration of AI/ML techniques into existing and new test suites to enhance test coverage, fault detection, and diagnostic precision.
Develop and deploy autonomous agents that execute complex test scenarios, analyze large volumes of test data, and autonomously identify and debug CPU failures.
Collaborate closely with CPU design, verification, and test engineering teams to understand architectural details and identify opportunities for AI-driven diagnostic improvements.
Evaluate, select, and guide the adoption of appropriate AI/ML frameworks, tools, and technologies.
Drive research, prototyping, and adoption of emerging AI methodologies applicable to CPU diagnostics.
Define and enforce best practices for code quality, scalability, performance, and long-term maintainability of AI-based diagnostic solutions.
Stay current with advances in AI, machine learning, and CPU architecture, and apply relevant innovations to diagnostic workflows.
Qualifications
Minimum
No minimum qualifications listed.
Preferred
Proven experience architecting and deploying AI/ML solutions in production environments.
Deep understanding of CPU architecture, functional validation, and manufacturing test methodologies.
Strong proficiency in programming languages such as Python, C++, or Java.
Extensive hands-on experience with AI/ML frameworks including TensorFlow, PyTorch, or scikit-learn.
Experience with data analysis and visualization tools for large-scale datasets.
Familiarity with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes) is a plus.
Experience with hardware–software co-design and system-level debugging.
Publications or technical presentations in relevant AI/ML or CPU architecture forums are desirable.
Experience applying reinforcement learning or other advanced AI techniques to autonomous systems.