Opportunities

AI Bootcamp is an initiative to help familiarize Caltech graduate students and researchers with machine learning concepts, tools, and techniques that they can potentially use in making their research more effective and efficient. We run one-week bootcamps that introduce introductory and advanced machine learning topics and include lectures and hands on sessions. TAs will help with organizing and operating the AI bootcamps. 

Applicants should plan on spending about 8 hours per week working as a TA. Hours are variable depending on Bootcamp schedule. There will be 5-6 bootcamps per year and the pay is $35/hour. This TA position offers Caltech students and postdocs a chance to earn extra cash while developing valuable teaching and organizational skills. Moreover, by working closely with researchers across diverse disciplines, TAs will broaden their understanding of how and where ML can be applied—gaining exposure to common patterns and the subtle nuances of applying machine learning in different fields.

If interested, please send a CV to [email protected]

AI Bootcamp TA - Primary Job Duties

Creating content for AI Bootcamp lectures and hands-on sessions (Google Colab)

• researching and summarizing content
• proofreading
• creating slides and graphics
• writing sample code and practice code and descriptions in Google Colab

Managing logistics of the AI Bootcamp

• Helping with setting up the audio/visual
• Helping with recording and processing the recorded sessions
• Other random tasks that can come up over the length of the bootcamp (or during the preparation)

Grading

• Creating and grading quizzes and codes submitted by participants

Attending lectures and hands on sessions

• Attending hands on sessions and helping participants with coding and debugging questions

Basic Qualifications

• Strong proficiency in Python
• Strong proficiency in building and using Pytorch
• Deep familiarity with Learning Theory and basic ML concepts 
• Experience with training and deploying ML models, especially neural networks
• TA experience or other teaching experience

Preferred Qualifications

• Industrial ML experience (or experience building and deploying large scale models used by other programmers)
• Familiarity and experience with advance ML systems (GNNs, RL, Foundational Models, Optimization techniques)