Date Posted: 11/02/2026 Req ID: 50894 Faculty/Division: Faculty of Arts & Science Department: Dept of Computer Science Campus: St. George Downtown Toronto Description: Course number and title: CSC490H1S - Capstone Design Project Please note, this position is a 0.25 FCE appointment. Course description: This course gives students experience solving a substantial problem that may span several areas of Computer Science. Students will define the scope of the problem, develop a solution plan, produce a working implementation, and present their work using written, oral, and if suitable video reports. Class time will focus on the project, but may include some lectures. The class will be small and highly interactive. Reference: https://artsci.calendar.utoronto.ca/course/csc490h1 Estimated course enrolment: 45 students Estimated TA support: one 60-hour TA position for every 30 students Class schedule: LEC5101: Wednesdays 18:00-21:00 *Please note, the delivery method for this course is currently in-person. Please note that, in keeping with current circumstances, the section delivery method may change as determined by the Faculty or the Department. Sessional dates of appointment: January 1, 2027 – April 30, 2027 Salary: Sessional Lecturer I = $7,062.93; Sessional Lecturer I Long Term = $7,897.25; Sessional Lecturer II = $7,897.25; Sessional Lecturer II Long Term = $8,453.29; Sessional Lecturer III = $8,453.29; Sessional Lecturer III Long Term = $8,720.29. Please note that should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail. Minimum qualifications: Graduate degree in Computer Science or closely related field required. Demonstrated expertise in topic area of the course required. Strong organizational, interpersonal, and communication skills required. Teaching experience at the university level or equivalent industry level required. Preferred qualifications: Previous experience teaching undergraduate courses in the field of Computer Science preferred. Demonstrated evidence of excellence in teaching preferred. Experience working with application and machine learning infrastructure, such as Kubernetes, Terraform, AWS/Azure/GCP. Experience fine-tuning language models using frameworks such as Tensorflow, Pytorch, Transformers. Experience serving LLMs with frameworks such as TGI/VLLM/SGLang. Experience prompting LLMs for a variety of classification and generation tasks. Experience building APIs in Python using frameworks such as Django, Flask, FastAPI. Experience deploying startup vs. enterprise-scale projects. Description of duties: Preparing and delivering the lectures in-person on campus as scheduled. Handling course administration including: maintaining the course website on quercus; developing marking schemes/syllabus; planning tutorial content when applicable; developing course assessments including assignments, projects, quizzes, tests, and final ass
