Become a Machine Learning Teaching Assistant
Each semester, the Machine Learning Department searches for qualified applicants for Teaching Assistant (TA) positions for all of our courses offered next semester. If you wish to apply, please do so using the form below, which includes questions about prior experience, course preferences, eligibility, etc. To receive full consideration, please submit your application three weeks before the last day of classes.
Some details about the matching / application process:
- Application and Matching Process: The application and matching process is centralized within MLD. There are three steps:
1. Students fill out this application form and express their preferences by ranking courses.2. Instructors review applications and express their preferences by ranking applicants.3. The MLD TA Coordinator finalizes the TA assignments based on instructor and TA applicant preferences, with the goal of meeting the needs of all our courses, while giving as many applicants and instructors their top choices as possible. This has two key implications: First, you may be assigned to any MLD course for which we believe you are qualified. Second, it is in your best interest to rank all the courses you are qualified for.
Instructors actively recruit for their individual courses -- as such, we encourage you to update your course ranking if your preferences change. Most TA assignments are made based on mutual interest in a match between TA applicants and the instructor teaching the course. If you want to TA for an MLD course, you should first fill out this form with as much helpful information as possible and speak to the instructor.
- Interviewing: You may be contacted by one or more professors for an interview. If so, don't delay! Interviews are a key step in the recruitment and application process. Some instructors recruit students over email and skip the interviewing step. If you are not contacted, feel free to reach out to the instructor of the course that most interests you. Being proactive is a great way to increase your chances for a TA-ship!
- Editing your Application Form: After submitting your application, you will receive a confirmation email with an "Edit Your Response" link. Save the email for your records. The link will allow you to make changes to your application if necessary.
- ITA Testing: All non-native English speakers are required to take the International TA (ITA) test the semester prior to teaching, or to have it waived via the TOEFL option below. There are fixed testing periods (usually in November and April). See the ITA registration schedule for details. PhD students may register for the exam at any time. However, we have to inform the testing center of our intent to hire any Master's students before they take the exam. If you are a Master's student and haven't taken the ITA test, please submit your application as early as possible so that we can arrange for you to take the test.
- TOEFL option: When you sign up for ITA Testing, you may hear about the TOEFL option. For MLD, we ask that you follow these guidelines:
1. If your TOEFL iBT Speaking Score was in the range 26 - 30, please select the TOEFL-option. You will automatically get you a Restricted I or a Pass.
2. If your TOEFL iBT Speaking Score was in the range 0 - 25, please select the standard ITA test option.
- Pay Structure: TAs are paid by either hourly wages or tuition/stipend coverage depending on their class level and home department. See the TA Pay Structure table for information.
- Time Commitment: On average across all MLD courses, hourly TAs tend to log 10-12 hours per week. But of course, this is high variance in that some weeks are a much higher or lower time commitment than others. As well, some TAs put in more time than others, and some courses ask more or less of their TAs. In that sense, you could equate the time commitment roughly to that of taking a course, which varies in many of the same ways.
- Questions: Direct any questions / suggestions to the MLD TA Coordinator (Matt Gormley, email@example.com).
Thanks ahead of time for your interest in teaching for the Machine Learning Department.