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Carnegie Mellon University

Instructional Staff - AI Engineer

등록일 : 2026-08-17
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기관명Carnegie Mellon University
홈페이지https://www.cmu.edu
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Description

Position Title: Instructional Staff - AI Engineer

Location: Heinz College, Carnegie Mellon University

Start Date: August 24, 2026 (Fall Semester)

Position Type: Part-Time, Temporary

Reports To: Faculty Supervisor(s)

Student-Facing: Yes

Position Summary

Heinz College at Carnegie Mellon University is seeking a graduate of the Master of Artificial Intelligence Systems Management (AIM) or an AI-related CMU program to assist in creating AI systems or workflows for assignments originally designed for Python developers into equivalent assignments for AI systems or LLM agent workflows. This role supports curriculum development and delivery for these courses, which include core courses in the AIM program and a related MISM course: 95-820 NL(X) and LLMs, 94-844 Generative AI Lab, 95-864 AI Model Development, and 94-879 Fundamentals of Operationalizing AI .

Key Responsibilities

  • Manage software, data and AI model compliance, including understanding university, local, state and federal policies to ensure course content, computing environments, computer resources, data, information and course delivery meets university, local, state and federal rules.

  • Establish, develop and maintain shared compute resources, such as virtual machines or local hosts, for implementing and evaluating Generative AI models.

  • Develop course assignments and activities to teach Generative AI model development, evaluation, operationalization, and standard industry practices for AI engineering.

  • Translate existing Python programming assignments into LLM Agent or ‘Agentic AI’ equivalents.

  • Collaborate with a supervising professor to align assignments with course objectives.

  • Maintain clear documentation of decisions and code structure.

  • Assist in testing and validating translated assignments for correctness and usability.

Additional Information

  • This is a student-facing, full-time temporary role focused on backend curriculum support.

  • The position is ideal for someone looking to contribute to academic development while gaining experience in educational technology and instructional design.

  • This is an AI-engineering focused position, supporting AI courses at Heinz College, Carnegie Mellon University. The position is ideal for someone who enjoys building systems, workflows or APIs for large language model (LLM) systems.

Qualifications Required Qualifications

  • Graduate of the AIM program at Heinz College, Carnegie Mellon University.

  • Willingness to present or communicate with students on a regular basis.

  • Proficiency in Python programming language and LLM System Engineering.

  • Experience establishing virtual machine and compute environments (Linux, AWS, GCP).

  • Strong understanding of distributed AI system concepts and infrastructure.

  • Experience with designing and conducting LLM evaluations and working with LLM APIs.

  • Ability to work independently and communicate effectively with faculty supervisor(s).

  • Ability to conduct worth with integrity and ethics and work professionally with students.

Preferred Qualifications

  • Experience with educational content development or curriculum support.

  • Experience with version control systems (e.g., Git).

  • Experience working with containers (Docker, Podman, Apple Containers)

  • Attention to detail and commitment to producing high-quality instructional materials.

Application Instructions Equal Employment Opportunity Statement

Carnegie Mellon University is an equal opportunity employer. It does not discriminate in admission, employment, or administration of its programs or activities on the basis of race, color, national origin, sex, disability, age, sexual orientation, gender identity, pregnancy or related condition, family status, marital status, parental status, religion, ancestry, veteran status, or genetic information. Furthermore, Carnegie Mellon University does not discriminate and is required not to discriminate in violation of federal, state, or local laws or executive orders.

접수정보

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