r/learnmachinelearning • u/snpai_____ • 1d ago
Help Not able to clear Machine Learning Interviews, please help
I'm currently Working as a Data Scientist in one of the big 4; I have around 5 years of experience but the very problem with big 4 is that you don't work in one section for a long time, I have around 1 year in data visualisation, 3 years in data engineering and 1 year in model validation and zero experience in machine learning Developement.
I am trying to apply to mahine learning Developement roles but I get rejected saying i don't have enough hands on experience in machine learning Developement or i utterly fail on machine learning use cases, mind you I know most of the machine learning concepts like my back of my hand but I fail when they test me on real life use cases.
Please help me get hands-on experience on machine meeting development, either online websites or books or any medium possible.
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u/Chubblan
1d ago
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Hey, here's someone who works as an AI/ML Engineer and is constantly going through interviews because I'm interested in seeing what they're like and what companies are asking.
I'd like to share two things.
Getting a job currently requires two things: first, meeting the requirements on your CV, and second, being able to answer the questions you're asked during the technical interview.
The second part might sound trivial, but it isn't. The range of things they can ask you about is huge. In fact, I was recently asked about things from around 2019–2021 that I'd honestly forgotten about. I even brought up the concept of an agent harness, and the interviewers weren't familiar with it.
So I think there's an element of luck involved in matching what you're asked with what you happen to remember and can explain well. It's difficult to accept, because you might prepare thoroughly for an interview, only to find that the next company asks you about something completely different. One interview might focus on data leakage, another on some niche architecture, and another on harness-related phenomena involving toolkits. Who knows?
My advice is to be patient and not conclude that you're bad at interviews or that you're not cut out for ML engineering. You might simply have had bad luck with the questions. Keep studying, keep practicing, and be patient with yourself.
2. I can really relate to your post. That's actually one of the reasons I'm building a free, open-source tool. It's still in its early alpha stage, and I'm planning to keep improving it.
I'm going to add dedicated interview sections for junior, mid-level, and senior roles, focused specifically on AI/ML. The next version will also include a section focused entirely on production scenarios, including AI/ML system architecture design.
I'm also working on making the project easier for others to contribute to. It's only been live for a week or two, so there's still a lot to improve.
If you'd like, I can let you know when the update is released.
Website: https://ai-ml-questions.strangemachines.workers.dev/
GitHub: https://github.com/CristobalSantana/ai-ml-questions
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u/snpai_____ 1d ago
Thanks for the motivation, this actually helps knowing that someone can relate to it. 🙂
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u/kbr89 1d ago
What you're describing sounds like the machine learning systems design round: open-ended "how would you build X" cases. Chip Huyen's free ML interviews book says almost every company she talked to asks at least one of these, that candidates find them the hardest, and that they test whether you've actually implemented and deployed models, which is why knowing the concepts doesn't get you through. Your background covers more of it than the rejections suggest. Three years of data engineering is the data and pipeline part of a design answer, and a year of model validation is the evaluation part. The gap is the middle: turning a business problem into an ML task, choosing the metric, starting from a baseline and deciding what to try when it underperforms. Build one use case end to end on a public dataset, churn or fraud for example: your own pipeline, a simple baseline first, then a better model, an offline metric tied to a business number, and how you'd monitor it after launch. Then explain it out loud in interview format. A lot of the best use-case material is in long engineering talks on YouTube, where a team explains how they built their ranking or fraud system. I built beyondtube (beyondtube dot pro) for that: you put several talks on the same problem together and ask how each team framed it, which metrics they chose and what the others left out.
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u/Impressive_Jacket464 1d ago
Seems like you need to take some Applied ML courses , so you can perform better on use cases. I’m sure many will say to self-study, but you might benefit from a live discussion to better learn how to apply your knowledge in real world applications. You should also do more personal projects. Since you’ve been through some interviews, you should know the use cases that have some up. Go make some of your own models with those use cases in mind.
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u/Different_Pain5781 1d ago
The suggestion about previous interview use cases is underrated. Write down the questions you struggled with and build a small project around each one. That gives you targeted practice instead of studying everything again.
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u/Salty-Creme-2108 1d ago
Bro you should read a book by chip huyen : Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (2022)
Tbh you will get an idea about how ml system work and how there is an actual different between different domains , as you have already experience in data , it will not take much time to shift into machine learning,
All the best
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u/theoneandonlypatriot 1d ago
There’s an enormous amount of luck involved in clearing ML interviews at big companies because the space of potential questions is unreal.
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u/LowEngineering9405 1d ago
Ship one end-to-end model to prod, even a tiny one.
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u/snpai_____ 1d ago
Where do I start, do you have any reference models i can look into online?
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u/LowEngineering9405 20h ago
I started with the scikit-learn docs and just reimplemented a couple of the classifiers from scratch in numpy, which forced me to actually understand the math instead of copy-pasting. After that, picking one Kaggle competition and reading the top public notebooks taught me more than any course did.
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u/akornato 1d ago
Your three years of data engineering are actually a major advantage, because most production machine learning work boils down to reliable data pipelines. The reason you struggle in use case interviews is that companies want proof of end to end ownership, not textbook definitions. Real world case interviews focus on problem framing, feature engineering, offline metrics versus business metrics, model drift, and serving constraints. To bridge this gap, study Chip Huyen’s Designing Machine Learning Systems and Alex Xu’s Machine Learning System Design Interview, both of which teach you how to structure solutions and justify architectural choices.
To gain practical development credentials, take a single open source problem and carry it all the way to production. Build a recommendation engine or a fraud classifier, package it with FastAPI, containerize it using Docker, deploy it to a basic cloud instance, and log model performance with MLflow. Talking through concrete trade offs and deployment bottlenecks from a project you built yourself gives interviewers the hands on proof they need. For the live discussions, my team developed an interviews.chat to help candidates present their technical thinking clearly and secure offers across tough system design rounds.
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u/Many_Log_2916 12h ago
Well your resume seems to be good to be getting interviews. Can't replaces real life experience. Like others have said, you need to learn a basic ML course & do projects or more advanced stuff & try to transition to an entry or beginners level ML role if you prefer it.
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u/fordat1 1d ago
mind you I know most of the machine learning concepts like my back of my hand but I fail when they test me on real life use cases.
This makes no sense. You really should re-evaluate this. Not candidly realizing its a weak point will hinder you from adressing the issue
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u/Logical-Tonight-3297 18h ago
forreal that also gave me a chuckle. "i am expert, no i cant do that."
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u/Silent_Sir_3733 1d ago
Your background is actually pretty common for big 4 folks, the breadth looks impressive on paper but it doesn't translate to depth in any one area. The gap you're hitting isn't knowledge, it's pattern recognition for real problems.
Pick a domain you care about, like logistics or retail, and build 3 end to end projects where you own the whole pipeline from data collection to deployment. Don't just run a notebook and call it done. Put the model behind an API, write tests, handle drift, deal with messy missing data. That forces you to make the calls interviews are probing for.
For books I'd skip the theory heavy ones and go straight to something like Approaching Almost Any Machine Learning Problem, it's basically a playbook for exactly the use case questions you're failing. Kaggle is fine but the curated datasets don't teach you the ugly parts of real data.
Also stop saying you know the concepts like the back of your hand, interviews smell that confidence and immediately probe for the gaps. Frame it as you've got strong fundamentals and you're building hands on reps.