84 coding-"https:"-"https:"-"https:"-"https:"-"https:"-"UNIV" positions at Carnegie Mellon University
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systems sufficient to maintain credibility with engineering teams (deep coding expertise not required). Experience navigating complex stakeholder ecosystems involving multiple contractors, oversight
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or develop applications or systems programs from detailed specifications. Code, test, and debug programs. Develop and maintain technical documentation. Flexibility, excellence, and passion are vital qualities
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with building codes Other duties as assigned Flexibility, excellence, and passion are vital qualities within the department of Facilities Management. Inclusion, collaboration and cultural sensitivity
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requirements. Supply & Safety Management: Requisition necessary parts and materials while ensuring all work complies with safety regulations, building codes, and university policies. Other duties as assigned
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Reinforcement Learning frameworks such as Areal or SkyRL. Familiarity with coding agentic frameworks (e.g., OpenHands, SWE-Agent) and software engineering benchmarks (e.g., SWE-Bench). A combination of education
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AI systems and how attackers adapt their tradecraft to exploit those vulnerabilities. Reverse engineer malicious code in support of high-impact customers, design and develop new analysis methods and
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childhood education, demonstrates commitment to Developmentally Appropriate Practice, and upholds the NAEYC code of ethics. The position is responsible for supporting the classroom educators to provide a
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software-intensive systems sufficient to maintain credibility with engineering teams (deep coding expertise not required). Experience navigating complex stakeholder ecosystems involving multiple contractors
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containerization and virtualization technologies, including VMware, VirtualBox, PodMan, and Singularity. Experience with CI/CD tools (Jenkins, GitLab CI, GitHub Actions) and infrastructure-as-code practices
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; comfortable developing production‑grade code and APIs. Solid understanding of ML theory, statistical learning, and common algorithms. Hands‑on experience with TensorFlow, PyTorch, Torch, Caffe, or similar deep