If you are serious about getting a machine learning job, you have probably asked yourself the classic question: Leetcode or Kaggle? You see people grinding hundreds of Leetcode problems for FAANG-style interviews. At the same time, you see others building Kaggle notebooks, winning medals, and sharing cool ML projects. It is easy to feel stuck and think you must master both perfectly before you even apply.
The truth is more nuanced. You do not need to become a Leetcode grandmaster or a Kaggle triple-gold medalist to land your first ML role. But you do need to understand what each platform is really for, how companies actually hire in 2025, and where Leetcode or Kaggle fits into a realistic preparation strategy. That is exactly what this guide will help you with.
You will see how different types of machine learning jobs treat coding interviews, ML fundamentals, projects, and competitions. You will also learn how to use Leetcode or Kaggle in a smarter way, so you do not waste months grinding the wrong things while ignoring what truly impresses recruiters and hiring managers.
What Leetcode or Kaggle Are Really For
Before you decide whether to focus on Leetcode or Kaggle, you need a clear picture of what each platform actually does and how it is perceived in the ML hiring world.
LeetCode is primarily a coding interview prep platform. You solve timed algorithm and data structure problems very similar to the ones asked in big tech coding rounds on platforms like LeetCode . When companies, especially big tech or product companies, test you in a “DSA round,” they usually mean something very similar to what you see on LeetCode. The focus here is on your ability to write correct, efficient code quickly, not necessarily on your understanding of machine learning theory.
Kaggle, on the other hand, is a platform for data science and machine learning competitions, datasets, and notebooks at Kaggle , where you work with real datasets and build end‑to‑end ML solutions You build models, tune hyperparameters, handle feature engineering, and think about evaluation metrics. On Kaggle, you show that you can take messy data and create a working ML solution that performs well on a leaderboard.
In simple terms, Leetcode or Kaggle represent two different types of skills. LeetCode is about core coding and algorithmic thinking. Kaggle is about applied ML and end-to-end projects. Modern ML hiring cares about both, but not always in the same way or to the same depth, and that depends heavily on the role and the company.
If you only do LeetCode, you might become good at algorithms but have nothing to show when someone asks about real ML projects. If you only do Kaggle, you might build great models but struggle to pass the initial coding screen. So instead of asking “Leetcode or Kaggle?” as an either–or choice, a better question is how to use both in the right proportion for the type of job you want.
Why “Leetcode or Kaggle” Is Often the Wrong Question
When beginners obsess about Leetcode or Kaggle, they are usually trying to find a shortcut. They hope that if they just do enough problems or competitions, they will magically become “ready” for any machine learning interview. Unfortunately, real hiring does not work that way.
Companies do not hire you because you solved a particular LeetCode problem or finished a specific Kaggle competition. They hire you because, together, your skills, portfolio, and mindset convince them that you can add value to their team. The platforms are just tools to help you build and demonstrate those skills.
Another reason the question “Leetcode or Kaggle?” is misleading is that machine learning jobs themselves are very diverse. A research-focused role in a big AI lab looks very different from a practical ML engineer job at a startup, which again looks different from a data scientist role in a non-tech company. Some of these will care a lot about algorithms; some will care a lot about end-to-end projects; some will put huge weight on your understanding of modern trends like transformers, LLMs, and MLOps.
When you only think in terms of Leetcode or Kaggle, you risk preparing too narrowly. You might become excellent at Kaggle-style tabular competitions but never touch NLP, computer vision, or recommendation systems, even if your dream company works heavily with those. Or you might do so many LeetCode problems that you forget to build a single real project that solves any meaningful problem.
A better mindset is to treat both platforms as part of a broader ecosystem. Your core should still be fundamentals: linear algebra, probability, optimization basics, classic algorithms, and practical ML workflows. Around that, you use Leetcode or Kaggle selectively, depending on whether you need to strengthen your coding muscles, your ML project experience, or both.
How Machine Learning Hiring Actually Works in 2025
If you want to decide how much to invest in Leetcode or Kaggle, it helps to understand what real interview processes look like today. While details differ between companies, there are some common patterns.
Most entry-level ML roles start with a resume screen. Recruiters and hiring managers quickly scan for signals: relevant degree or strong self-taught story, solid programming skills, ML courses, and most importantly, completed projects. This is where having public work on GitHub, Kaggle notebooks, or personal projects makes a big difference. It shows you can go beyond theory.
Next, many companies have a coding screen. This can be a timed online test with LeetCode-style questions or a take-home assignment. Even for ML roles, they often want to ensure you can write clear, efficient code in Python, handle arrays, hash maps, and basic algorithms, and reason about complexity. Here, a moderate amount of LeetCode practice is genuinely helpful.
After that, serious ML interviews dig into your understanding of machine learning. You might get questions on supervised vs unsupervised learning, bias-variance tradeoff, regularisation, evaluation metrics, feature engineering, and modern architectures. Increasingly, companies also ask about deep learning frameworks (PyTorch, TensorFlow), transformers and large language models, and how you would approach fine-tuning or prompt engineering in real scenarios.
Practical interviews are also more common now. Instead of only asking theory, many teams expect you to walk through an end-to-end project you have done. They want details: how you cleaned the data, how you chose the model, how you handled overfitting, how you monitored performance, and how you would deploy or maintain the model. Kaggle-style work is useful here, but real or realistic end-to-end projects are even better.
Finally, in 2025, more companies are thinking in terms of MLOps and production readiness. Even at a junior level, you get bonus points if you can talk about model deployment, monitoring, drift, CI/CD for ML, or tools like MLflow, Kubeflow, or cloud ML services from AWS, GCP, or Azure. You do not need to be an expert yet, but being aware of these trends shows that you understand where the field is going.
When you see the whole picture, it becomes clear that Leetcode or Kaggle are just pieces. They matter, but they are not the whole story. A good preparation strategy aligns your efforts with the actual stages of the hiring funnel: resume, coding, ML knowledge, and real-world impact.
Using Leetcode or Kaggle Strategically at Different Stages
Instead of spending all your free time on either Leetcode or Kaggle with no clear plan, it is much smarter to use them differently at different stages of your journey.
If you are in the early learning phase, you should focus first on Python basics and core ML concepts. At this stage, LeetCode can be overwhelming, and Kaggle competitions might feel like magic scripts you barely understand. Here, you can still touch both platforms, but gently. You might solve easier LeetCode problems just to get used to writing code under constraints, and you might explore beginner Kaggle notebooks to see how people structure an ML workflow.
Once you are comfortable with Python and have completed a couple of small ML projects, you can dial up the intensity. Now, using LeetCode for targeted practice makes more sense. You do not have to reach “top 1%” level. For most ML roles, being solid at medium-level problems and common patterns is enough. The goal is to ensure that a basic coding round does not block you from showing your ML strengths.
In parallel, you can use Kaggle more actively, but with a specific goal. Instead of obsessing about your final leaderboard rank, focus on learning how to handle different problem types. Try a tabular competition to strengthen feature engineering. Try a vision competition to practice CNNs or transfer learning. Try an NLP challenge to understand tokenisation, transformers, and Hugging Face-style workflows. Treat each as a project you can later explain in interviews.
As you move closer to applying for jobs, your use of Leetcode or Kaggle should become even more selective. If you know your target companies are heavy on algorithm rounds, you might schedule short daily LeetCode sessions to stay sharp. If you know they care a lot about open-source or research, you might reduce Leetcode and spend more time polishing a strong Kaggle project or personal ML app.
The key idea is this: Leetcode or Kaggle are tools, not identities. You are not “a LeetCode person” or “a Kaggle person.” You are an aspiring ML professional using these platforms intentionally to strengthen specific muscles at the right time.
Common Mistakes Beginners Make with Leetcode or Kaggle
Since Leetcode or Kaggle are so visible on social media, it is very easy to copy what everyone else seems to be doing without thinking about your own goals. That often leads to predictable mistakes.
One common mistake is grinding LeetCode for months without building a single ML project. You might get very fast at solving abstract tree or graph problems, but when an interviewer asks, “Tell me about a machine learning project you worked on,” you have nothing concrete to describe. For ML roles, that is a red flag. Recruiters may assume you are more interested in generic software engineering roles than in applied ML.
The opposite mistake also happens. Some beginners spend all their time on Kaggle, joining dozens of competitions and copying top solutions without deeply understanding them. They end up with messy notebooks, lots of libraries they cannot fully explain, and a false sense of confidence. In interviews, when someone asks “Why did you choose this model?” or “How would you deploy this?” they struggle to answer.
Another issue is treating Leetcode or Kaggle as a checklist. You might think, “If I have 500 LeetCode problems and three Kaggle medals, I am guaranteed a job.” Unfortunately, there is no such guarantee. Hiring is affected by market conditions, competition, and how well you match a specific team’s needs. Your preparation increases your chances, but it does not create entitlement.
A more subtle mistake is ignoring new trends. In 2025, many teams care about large language models, generative AI, and practical ways to integrate them into products. If your entire ML profile is based on older tabular competitions and you never mention transformers, LLMs, or modern libraries like Hugging Face, you can look outdated even if your Kaggle profile is strong.
Finally, some people get trapped in comparison. They see others on social media posting about insane LeetCode streaks or Kaggle grandmaster titles and assume they are already behind. This leads to burnout and a feeling that nothing they do is enough. In reality, most junior ML roles are filled by people with a balanced profile: good fundamentals, a few solid projects, and decent but not extreme performance on Leetcode or Kaggle.
Designing Your Own Machine Learning Job Roadmap
To escape the Leetcode or Kaggle confusion, you need your own roadmap that fits your background, timeline, and target roles. You do not need a perfect plan, but you do need a direction.
Start by defining the kind of ML work that excites you. Are you more interested in recommendation systems, NLP with LLMs, computer vision, time series forecasting, or experimentation in product teams? When you have a rough idea, you can choose projects and Kaggle datasets that align with those interests instead of jumping randomly between competitions.
Next, evaluate your current coding level. If you struggle with basic loops, recursion, or data structures, then you should give LeetCode or similar platforms a period of focused attention. Not because you want to be the best, but because you need a baseline so coding rounds do not wipe you out. Once you reach that baseline, you can maintain it with lower effort while you emphasise ML work.
Then, design a sequence of projects. For example, you might start with a simple tabular classification project, then move to a CNN-based image classifier using transfer learning, then build a small NLP app that uses a pretrained transformer or API from OpenAI, Google, or another provider. Each project should teach you something new and be explainable in detail. This is where Kaggle can be a treasure trove of datasets and starter notebooks.
As you build projects, make sure you document them well. Write clear READMEs, clean your code, and add short writeups of what you tried, what worked, and what did not. This documentation becomes part of your portfolio and makes you look far more professional. When you are ready to think about adjacent roles or stepping stones, you can also learn how data-heavy roles like analytics fit into the bigger picture. At that point, reading something like Data Analyst Career: 7 Essential Trends for 2025 can help you understand how ML and analytics careers overlap and complement each other.
Finally, remember that interviews are not just about your repositories or your Leetcode or Kaggle stats. They are about how you think, how you communicate, and how you work with others. Practise explaining your projects out loud, as if you are talking to a smart friend who does not know ML. Be honest when you do not know something, but show that you know how you would go about finding an answer.
Final Thoughts: Leetcode or Kaggle Is Only Part of the Story
When you look at the big picture, the question is not simply Leetcode or Kaggle. The real question is how you can become the kind of machine learning professional who understands fundamentals, writes solid code, builds real projects, and stays aware of modern trends like LLMs, generative AI, and MLOps.
LeetCode helps you build coding speed and confidence under pressure. Kaggle helps you practise applied ML and experiment with different techniques and datasets. Both can be powerful, but neither is a magic ticket. What matters is how intentionally you use them and how well they support your broader roadmap.
If you keep your focus on learning deeply, building things that actually work, and communicating your work clearly, you will naturally make better decisions about where Leetcode or Kaggle fits into your journey. Instead of copying someone else’s grind, you will be designing a preparation path that makes sense for you—and that is exactly what recruiters and hiring managers notice when they decide who gets an offer.
FAQ: Leetcode or Kaggle for Machine Learning Jobs
Do I need both Leetcode and Kaggle to get a machine learning job?
You do not strictly need both, but using each one for its purpose is very helpful. Leetcode builds your coding and algorithm skills for interviews, while Kaggle gives you practical ML experience and projects you can talk about. Together, they form a stronger profile than either alone.
How much LeetCode is enough for ML interviews?
For most entry-level roles, being comfortable with easy and medium questions on topics like arrays, strings, hash maps, trees, and dynamic programming is usually enough. You do not need to hit thousands of problems. Aim for consistent practice and understanding, not raw problem count.
Do Kaggle medals really impress recruiters?
Kaggle medals can be a positive signal, but they are not everything. Recruiters care more about whether you can clearly explain what you did, why you did it, and how it relates to real-world problems. A smaller number of well-documented projects can be more impressive than many half-finished competitions.
Should I focus more on Leetcode or Kaggle if I want to work with LLMs and generative AI?
If your goal is LLM and generative AI work, Kaggle-style projects and personal experiments with transformers, fine-tuning, and prompt engineering will likely matter more. However, basic LeetCode-style coding skills are still important so you can pass general coding screens and write robust glue code around models.
I feel overwhelmed by others doing so much more on Leetcode or Kaggle. What should I do?
It is easy to get discouraged by social media. Focus on your own roadmap instead. Set realistic weekly goals, build one project at a time, and use Leetcode or Kaggle to support those goals rather than define your entire identity. Over a few months, consistent progress matters far more than extreme but unsustainable grinding.
