Master The Machine Learning Interview As A Software Engineer
Everyone wants to break into machine learning. Very few actually do.
ML roles are some of the most coveted—and competitive—jobs in tech. But here’s the truth: most software engineers trying to transition into ML fail the interview. Not because they’re not smart enough, but because they prepare the wrong way. They memorize buzzwords. They build side projects nobody cares about. They don’t know how to connect their skills to what top companies actually evaluate.
This course changes that.
Master the Machine Learning Interview as a Software Engineer is your unfair advantage in a crowded field. It’s a step-by-step, no-fluff guide to mastering every part of the ML interview process—from modeling and coding to system design and behavioral rounds. Built by a working ML engineer who’s helped over 200 people land roles at companies like Meta, Amazon, and Google, this course gives you the exact strategies, examples, and mental models that interviewers want to see.
You’ll learn how to:
- Speak clearly and confidently about core ML concepts—without rambling or sounding rehearsed
- Tackle real-world modeling and notebook interviews like a pro
- Implement algorithms from scratch and explain every line
- Design ML systems that scale—and impress senior engineers
- Tell impactful project stories that make hiring committees remember your name
Whether you’re switching from software engineering, returning to ML after a break, or aiming for your first senior role, this course gives you everything you need to stand out and win offers.
Don’t just hope you’ll break into ML. Learn how to make it happen.
Dr. Yayun Jin is a machine learning engineer at Reddit, where she leads the development of large-scale ML systems that support Reddit’s advertising products. She previously worked at Microsoft and Workday, building production ML and generative AI solutions across enterprise and consumer domains. With a PhD in engineering and years of hands-on experience, Yayun blends research rigor with real-world engineering execution. She has also mentored over 200 engineers and students, helping them land ML roles at companies like Google, Meta, and Amazon.
This course distills the frameworks and strategies she’s refined through industry work and one-on-one coaching. Designed for software engineers preparing for ML interviews, it offers a clear, actionable path through the technical, design, and behavioral expectations candidates face today. Learners walk away with insider insights, real-world examples, and the confidence to excel.
Follow Yayun on LinkedIn: https://www.linkedin.com/in/yayunjin/
tl; this course has:
1/ Essential ML concepts to know and tructure of ML interview process from system design, behavioral, coding, and project deep dive
2/ What knowledge to be expected to cover for various ML roles at different career stages
3/ Especially helpful information on how to structure and answer questions
For the longest time, ML and AI interviews have lacked transparency and over the years, I remember so many friends would tell me the most varied responses on the types of questions they would encounter in the interview process. From the start, it was incredibly helpful to see how this course breaks down the structure of the interview process that software engineers working in machine learning typically go through. It covers everything you need to know, ranging from project walk-throughs, system design, behavioral questions, key algorithms, coding examples (notebook interview examples), preparation techniques, and more.
The course is packed with valuable insights—Yayun guides us through different study plans tailored to various ML roles and outlines expectations for what candidates should know and how to answer specific questions at different career stages.
One of the best things about the course is that Yayun talks about strategies for communicating technical explanations clearly, without unnecessary complexity. I found this really helpful for those who struggle with "soft skills" or have difficulty articulating high-level abstractions and describing engineering tradeoffs in decision-making. She drops gems on how candidates can present their thought processes in a more approachable way and with authority and smooth delivery.
There are great examples of how to improve the quality of responses to interview questions and how to deliver them effectively. Yayun also provides concrete methods for structuring answers to commonly asked interview questions. She describes CLEAR (Conceptualize, Link, Example, Advantages/Disadvantages, Result) framework, builds upon the STAR method and extends it to STAR+R (Reflection) offering an easy-to-remember approaches on discussing ML concepts and use cases.
I truly enjoyed taking this course, it's a must-watch for anyone going into the job hunt and interview prep in AI and ML.
Lastly, I really liked having notes on every video, super helpful and as I was taking my own notes to make sure I didn't miss anything.
This was a very informative course!
This resource is very well-organized and easy to follow. The explanations are clear, practical, and directly relevant to real interview scenarios. Highly recommended for anyone preparing for ML interviews
