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Designed and taught by FAANG+ engineers, this course will give you a foolproof preparation strategy to crack the toughest interviews at FAANG and Tier-1 companies.
Covering data structures, algorithms, interview-relevant topics, and career coaching
Technical coaching, homework assistance, solutions discussion, and individual session
Live interview practice in real-life simulated environments with FAANG and top-tier interviewers
Constructive, structured, and actionable insights for improved interview performance
Resume building, LinkedIn profile optimization, personal branding, and live behavioral workshops
If you do well in our course but still don't land a domain-relevant job within the post-program support period, we'll refund 50% of the tuition you paid for the course.*
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If you want to secure a role as a machine learning engineer at a renowned tech company, Interview Kickstart’s Machine Learning (ML) Interview Prep Masterclass is for you! The course is designed and taught by seasoned professionals from FAANG+ companies (Facebook, Amazon, Apple, Netflix, and Google) who share real-world industry insights based on their firsthand experience in conducting interviews at leading tech firms. Read More
Over several weeks, you will learn about how to present your knowledge of data structures, algorithms, system design, and core machine learning concepts during your interview. You will be able to demonstrate that you not only understand theoretical concepts but also can apply them practically.
As part of your ML interview prep, you will also be able to take part in mock interviews with experienced engineers from top tech companies. These sessions simulate actual interview conditions, giving you an unrivaled opportunity to get in front of the very people who conduct interviews on behalf of leading tech companies to fill the roles.
Our machine learning interview course is ideal for professionals or beginners who are looking to break into or advance within the field of machine learning. Whether you’re a software engineer with some machine learning experience or a data scientist going for a promotion, this course will help improve your confidence during the interview itself.
If you’re preparing for interviews at companies like Google, Meta, Amazon, or a startup, this course is especially useful for candidates who want a guided path through both the fundamentals and advanced concepts of machine learning while also developing their problem-solving and system design skills.
This course is also a great fit for mid-level engineers transitioning into machine learning-focused roles. You will receive advice on how to structure your answers, present your experience, and tackle the kinds of questions that come up in machine learning mock interviews. The course doesn’t just teach technical content; you’ll also learn how to succeed in interviews.
Recent graduates or master’s students with strong foundations in ML and programming can also benefit by learning about interview strategies beforehand and understanding what real-world ML roles demand.
Lastly, if you’ve not interviewed for a while or lack confidence in technical interviews, this ML interview prep course provides a supportive, feedback-driven environment to rebuild your skills and gain confidence. The blend of theory, practical application, and mock interviews ensures you’re ready for the real thing.
If you’re serious about landing a role in machine learning, this machine learning interview course is a solid investment in your career and future.
Interview Kickstart programs are designed with working professionals in mind; as such, the course offers a flexible schedule that accommodates people living in various time zones and those who have other commitments. Live sessions are conducted on weekends and evenings, allowing participants to balance daily responsibilities while engaging in intensive machine learning interview prep. The program also includes self-paced study materials and weekly assignments to reinforce learning and practice.
Enroll in our machine learning interview prep course today and open the door to endless opportunities. Read Less
Typically, the Machine Learning interview process at FAANG+ and other Tier-1 companies include the following rounds:
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This can be a combination of basic ML understanding round/past projects or purely coding-based: Medium Hard LC questions. Some companies refuse to move forward if you fail the initial ML screen.
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Given an infinite chessboard, find the shortest distance for a knight to move from position A to position B.
Implement a k-means clustering algorithm with just NumPy and Python built-ins.
Given a filter and an image, implement a convolution. Follow up with a given stride length, padding, etc.
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For more such questions, read 50+ Machine Learning Interview Questions and Advanced Machine Learning Interview Questions You Should Practice.
Machine Learning has changed the face of technology as we know it. Machine Learning adoption results in 3x faster execution and 5x faster decision-making. As a result, not only are ML engineer positions in high demand, with companies willing to pay top dollar for the right engineers, but the responsibilities for these roles have become significantly more diverse.
When a company hires ML engineers, it wants candidates who can contribute to innovations that will change the world.
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Machine Learning Engineers are highly skilled programmers who develop Machine Learning systems for business applications. They scale prototype models to large datasets, deploy them on the cloud or internally, and build end-to-end pipelines to continuously monitor the model performance.
Even though the specific responsibilities of ML Engineers may vary considerably, their key day-to-day jobs may include all or a subset of the following:
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Basic Qualifications
Preferred Qualifications
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In a Tier-1 company, the typical career ladder for the ML role looks like this:
Machine Learning Engineer roles at Facebook are highly rewarding, both in terms of compensation as well as professional growth. The different levels of Machine Learning Engineers at Facebook are:
The company divides the ML Engineer roles into different levels:
ICT2: Apple’s entry-level position which usually attracts people with 0-1 year of experience. They need to have at least some knowledge of ML modeling with proficiency in Python.
ICT5: Senior ML Engineers with 10+ years of experience are hired at this level. They are expected to manage their own teams within the organization or work with cross-functional teams.
ICT6: Highly experienced people with experience in managing multiple teams are usually hired at this level.
Unlike other companies such as Amazon and Apple, Netflix doesn’t have job levels. The company is known mostly for hiring only senior professionals with at least 4 years of experience. They have also started hiring new graduates for software engineer positions recently.
Here are the median salaries of a Software Engineer at Netflix working in the ML/AI domain:
The different job levels at Google:
L3 (ML Engineer II): An entry-level position with 0-1 year of experience
L4 (ML Engineer III): Requires 2-5 years of experience
L5 (Senior ML Engineer): Requires over five years of experience
L6 (Staff ML Engineer): Requires 5-8 years of experience
L7 (Senior Staff ML Engineer): Requires over 8 years of experience
Knowing the Machine Learning Engineer’s salary details for other tier-1 companies can help you evaluate your options better. We’ve curated the salaries associated with each of these companies at different levels:
What are the programming languages used in Machine Learning?
Machine Learning modeling is typically done in Python, which has excellent support from inbuilt libraries to do the same. R is another programming language used for experimentation purposes, but it’s not as widely used as Python. Some companies might also use MATLAB.
Is having a mathematics background a must for ML-related roles?
While it is not a must, having familiarity with concepts such as probability, integrals, differentiation, vectors, coordinate geometry, etc., can assist in understanding the idea behind several ML algorithms.
Do ML Engineers perform ML modeling/experimentations, or are they just concerned with the deployment part?
It depends on the role. Many companies expect MLEs to handle modeling, experimenting, and deployment parts. In contrast, other companies have data scientists to perform ML experiments and MLEs to translate those python ML models to binaries for deployment.
Is IK’s Machine Learning Interview Course just for professionals working as ML Engineers in non-FAANG+ companies?
I am working as a Data Scientist in my current company. Will this course help me transition into an ML Engineer role?
That depends. If you have some practical experience in deploying Machine Learning models on a production scale with working knowledge of platforms like AWS, Azure, or GCP, then this course can help you fill in the gaps required for an ML Engineer role. We will cover the relevant Data Structures and Coding, Scalable System Design, and ML System design concepts that you will need to crack the interviews. Additionally, we will also help you modify your resume to highlight your ML Engineer relevant experience to recruiters.
Is this Machine Learning Interview course suitable for freshers?
No, this course is for working professionals with at least two years of experience working as an ML Engineer or Software Engineer working on ML projects. Additionally, if you are a Data Scientist with practical experience in deploying ML models, you can join the course to transition into the ML Engineer role.
Why do we need to learn Scalable System Design concepts for an ML Engineer interview?
Scalable system design, specifically ML system design, is an integral part of this role. ML Engineers are required to go through at least 1 or 2 system design rounds. ML Engineers build on the concepts learned from deploying a general software and combine it with the knowledge of ML algorithms to deploy ML models on a production scale. In our course, we cover both scalable system design in 3 weeks and ML system design in every live class of the ML Masterclass.
How hard are the coding questions asked in ML Engineer interviews?
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