Teaching Assistant – Machine Learning

About Us

The name Interview Kickstart might have given you a clue. But here’s the 30-second elevator pitch – Interviews can be challenging. And when it comes to the top tech companies like Google, Apple, Facebook, Netflix, etc., they can be downright brutal. Most candidates don’t make it simply because they don’t prepare well enough. InterviewKickstart (IK) helps candidates nail the most challenging tech interviews.

We have recently launched an 11-month-long Machine Learning program to help professionals switch careers to Machine Learning Engineering roles. 

We are looking for Teaching Assistants(TAs) who can support our students with their doubts/questions in our online Machine Learning Engineering programs. 

The TAs should be well-versed with conceptual and practical knowledge on at least 2-3 topic categories listed below with hands-on experience in Machine Learning skills as mentioned. 

• Curriculum Outline 
• ML Maths 
• Essentials of Probability
• Probability Distributions
• Essentials of Statistics
• Calculus Primer
• Linear Algebra and Regression
• Classical ML
• EDA & Feature Engineering
• Regression Algorithms
• Classification Algorithms
• Bagging & Boosting Techniques
• Unsupervised Machine Learning
• Deep Learning
• Intro to Neural Networks
• Deep Learning Tools & Frameworks ~ Tensors & Operations, TensorFlow etc.
• Neural Architectures
• Computer Vision
• Computer Vision
• Natural Language Processing
• Modern ML Architectures
• Natural Language Processing
• Generative AI & RLHF
• Generative AI
• Reinforcement learning from Human Feedback (RLHF)
• MLOps
• MLOPs – Basics of Software System Design
• MLOPs – ML Design Principles
• MLOPs – ML Project Scoping
• MLOps – Model Training
• MLOPs – ML Model Deployment
• MLOPs – Model Performance and Re-training
• MLOPs – Model Monitoring and Diagnosing Production Failures

Requirements: 

• To be an effective machine learning subject matter expert, the requirements typically include a combination of in-depth technical expertise, teaching experience, and communication skills. 
• Should have hands-on experience in any one of the following: 
• Python for machine learning
• Mathematics for Machine Learning
• Classical ML
• Deep Learning – Neural Networks, Neural Architecture & Modern ML Architecture
• Computer Vision
• Natural Language Processing
• Generative AI
• Reinforcement Learning 
• MLOps
• Minimum three years of experience in tier-1 Tech companies as an ML Engineer or Scientist
• OR BE, ME or PhD students in tier-1 colleges with a strong understanding of ML & Math concepts.
• Ability to simplify complex topics and engagingly explain them
• High levels of empathy to understand the challenges faced by students and willingness to help them out
• PhD in Computer Science/Applied Mathematics/Statistics is preferred.

TAs are engaged in the program in the two following ways. 

1- Offline Doubts Resolution 

Process Outline – Offline Doubts 

• Students will raise their doubts/questions from our learning platform. 
• The IK operations team will assign you the doubts(ticket). You will receive it as an email thread. This thread is also referred to as a ‘ticket’. 
• Once you receive the ticket, you should provide the student’s first response within 12 hours.
• After you have provided your response to the query, the students can re-open the ticket and the thread may continue if the students are not satisfied with the response or have some follow-up questions. 

Overall Expectation – Offline Doubts 

• Maintaining a 12-hour TAT for giving an appropriate answer on tickets assigned to you. 
• Empathising with the student and taking a student-centric approach to resolving their doubts by providing them with detailed, on-point and easy-to-understand responses. 
• Aligning with the IK’s machine learning content to understand the context behind the ticket to resolve it.
• Be available promptly for any follow-up questions the student may have on your response. 
• Taking the initiative to improve processes, supporting the internal IK team on requirements and advance notice in case of un/planned unavailability. 

Payment Process – Offline Doubts 

• The payment is per ticket that you will close with the student. 

2- Online Doubts Resolution – During Live Class 

Process Outline – Online Doubts during live class 

• As part of the online support to the student, TAs also join the Zoom live classes for the program with the instructor/teacher. 
• The instructor/teacher will primarily teach the material, and TAs will monitor the Q&A section and resolve student doubts posted in the Q&A section without interrupting the flow of the instructor/teacher. 

Overall Expectation – Online Doubts during the live class 

• Being available for the live class timing of the US-based cohort – Sunday, 9 AM – 1 PM PST. (The course may extend by ~1 hour) 
• Empathising with the student and taking a student-centric approach to resolving their doubts by providing them with detailed, on-point and easy-to-understand responses. 
• Getting yourself thoroughly familiar with the live class content before the live class to better understand the context behind the ticket to resolve it.
• Be available promptly for any follow-up questions the student may have on your response. 
• Taking the initiative to improve processes, supporting the internal IK team on requirements and advance notice in case of un/planned unavailability. 

Payment Process – Online Doubts during live class 

• TA will be compensated basics the time they spend during the live class.

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Do’s

• Provide students with detailed, on-point and easy-to-understand responses.
• Ask for clarification from the student if their doubts/question is vague but ensure minimum back and forth. 
• If required, research the topic well while preparing your response. 
• Make sure your response is grammatically and syntactically correct. 
• Only share external links with your original response as part of the additional reference. 
• Taking the help of ChatGPT to frame an answer is okay unless that answer is aligned with the requirement of the students and the do’s and don’t mention here. 

Don’t

• Avoid providing students with external links to read or refer to independently.
• If unsure about any topic/technicality, please avoid giving half-baked responses. You ask the Ops PoC to assign someone else. 
• Avoid giving AI-generated answers as it is to the students – If you are using a tool like ChatGPT – you need to carefully verify the response and edit/add/modify the generated answer to ensure it is detailed, on-point and easy to understand. 

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