TA336599- Fall 2025-CIS*6020 Artificial Intelligence

Teaching Assistant Work Assignment
Posting Details
Type of Work Assignment and Bargaining Unit: 
GTA, Unit 1
Academic Unit: 
School of Computer Science
Semester(s) of Assignment(s): 
Fall 2025
This Work Assignment(s) may be assigned to fulfill the terms and conditions of a guaranteed Job Security Period: 
N/A
Course Details
Course Number: 
CIS*6020
Course Name: 
Artificial Intelligence
Course Format: 
In-Class
Course Description: 
See Course Calendar
Projected Class Enrolment: 
45
Level of Appointment for GTA, GSA-1 UTA(s)
The following is an estimate of number and levels of available appointments at the time of posting. This may change (increase or decrease) at the time of assignment contingent on departmental needs.
This Posting includes Multiple Work Assignments: 
No
Number of Available 0.25 (35 hour) Work Assignments: 
0
Number of Available 0.5 (70 hour) Work Assignments: 
1
Number of Available 0.75 (105 hour) Work Assignments: 
0
Number of Available Full 1.0 (140 hour) Work Assignments: 
0
Total Number (Load) of Assignment(s) Available: 
1
Duties and Responsibilities
Anticipated Duties and Responsibilities: 
Orientation-Training
Office Hours
Preparation
Student Consultation
Attending Lectures
Email Correspondence/Monitoring
Conducting Labs/Seminars
Meetings
Invigilating Exams
Grading
Qualifications
Required Qualifications: 
• As per the Collective Agreement, must be enrolled as a student in the Fall 2025 semester. • Must have excellent organizational, time management and communications skills. Must be available to attend and conduct scheduled office hours throughout the entire semester, attend weekly meetings, if applicable, as assigned by the instructor. • Must have excellent writing skills (as evidenced by peer reviewed publications) and be able to effectively critique student assignments in writing. • Must have excellent verbal communications skills (as evidenced by a history of presentations at various venues). • Must have strong familiarity with the principles of Academic Integrity. • Must provide evidence of competence in data management in a Unix environment, including: (a) the ability to load and manipulate structured text data, including delimited files, using the Python programming language, (b) the ability to manage files and directories on Unix system, (c) control processes and (d) transfer files. • Must be a current graduate student (MSc or PhD) in Computer Science. • Must have completed graduate-level coursework or research in Artificial Intelligence or Machine Learning. • Must demonstrate a working knowledge of course topics such as intelligent agents, search algorithms, constraint satisfaction, knowledge representation, probabilistic reasoning, deep learning, and reinforcement learning. • Must be proficient in Python, and able to support students working with AI/ML libraries such as Scikit-learn, TensorFlow, or PyTorch.
Preferred Qualifications: 
• Experience as a teaching assistant or grader for a senior undergraduate or graduate AI or ML course. • Familiarity with the textbook Artificial Intelligence: A Modern Approach (4th Edition) and related teaching materials. • Experience with Jupyter Notebooks, GitHub, and collaborative code review workflows. • Experience supporting or evaluating student research projects involving applied AI/ML, or time-series data. • Exposure to tools and concepts used in explainable AI (e.g., SHAP, attention visualization). • A publication or thesis related to Artificial Intelligence or Machine Learning.
Days Required and Wages
Days and Times Required: 
See Webadvisor for lab/lecture details.
Period of the Work Assignment (Start Date and End Date): 
September 2, 2025 to December 19, 2025
Wage Rate (per hour, per semester, per Full-load 1.0 Work Assignment): 
GTA $49.43/hr; $6,924.98/semester effective 2025/26
Other Posting Information
Application Deadline (All postings will automatically expire at 11:59 pm on this day): 
Monday, July 7, 2025
Posting Email Contact: 
socspost@uoguelph.ca
Hiring Contact Information: 
Lauren King, Instructional Support Coordinator - teachingsupport@socs.uoguelph.ca

At the University of Guelph, fostering a culture of inclusion is an institutional imperative. The University invites and encourages applications from all qualified individuals, including from groups that are traditionally underrepresented in employment, who may contribute to further diversification of our Institution. For more information, the Office of Diversity and Human Rights (DHR) is a welcoming, safe and confidential one-stop shop for information, training and support on issues relating to diversity and human rights on our campus.

All applicants must be eligible to work in Canada specifically at the University of Guelph before applying for an academic work assignment. All successful applicants must perform their work in Ontario and must be able to attend on-campus in-person meetings as required.

Teaching Assistant work assignments are unionized with CUPE 3913 and their terms and conditions of work are covered by the Unit 1 Collective Agreement between the University and CUPE 3913 (email contact: president@cupe3913.on.ca).

The use of AI to perform job responsibilities must be directed by or approved beforehand in writing by the supervisor or course instructor.