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Translated from Spanish .
What should be my steps to become a Machine Learning Engineer?
What should be my steps to become a Machine Learning Engineer?
Hello, my name is Daniel and I'm heavily interested in AI, that I want to make it part of my career! I know that I have to really focus on math and learn Python well. I would just like some other opinions/help for what I can do to get into MLE as my career. Also, I'm planning to get into CS major at UW (I'm still in my senior year at Highschool taking College courses that include Java and Math). Thank you for reading!
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10 answers
Updated
Sandeep’s Answer
Hi Daniel, you are absolutely right to focus on math and Python. In addition to that, I recommend:
- Building strong CS fundamentals (data structures, algorithms, OOP).
- Learning core machine learning concepts
- Working on hands-on projects (small ML models, data analysis, Kaggle) to apply what you learn.
- Getting comfortable with the full ML pipeline: data cleaning, model evaluation, and basic deployment.
- Getting involved in clubs, research, and internships once you start at UW.
You are already ahead by taking college-level CS and math courses in high school. Keep building consistently, stay curious, and focus on fundamentals, you will be well-prepared for an MLE career.
- Building strong CS fundamentals (data structures, algorithms, OOP).
- Learning core machine learning concepts
- Working on hands-on projects (small ML models, data analysis, Kaggle) to apply what you learn.
- Getting comfortable with the full ML pipeline: data cleaning, model evaluation, and basic deployment.
- Getting involved in clubs, research, and internships once you start at UW.
You are already ahead by taking college-level CS and math courses in high school. Keep building consistently, stay curious, and focus on fundamentals, you will be well-prepared for an MLE career.
Hello Sandeep, thank you a lot for your comment! I appreciate it :)
Dani
Updated
Siva’s Answer
Hello Daniel,
It’s great that you are thinking about this early — that already puts you ahead. You are right about focusing on math and Python, which is critical and I want to emphasize something important: learning ML is not about going through tutorials; it’s about solving real problems.
Here are some practical approaches to your ML journey:
1. Pick a real-world problem you actually care about. It could be in health, sports, climate, finance, education or anything that motivates you. Because, ML takes time and patience. Passion matters.
2. Use AI as your learning partner to help design the solution, explain algorithms, and review your code. But make sure you understand why things work. Your goal is to become a strong problem solver, not a model user.
3. Build small, end-to-end projects. Even simple projects teach more than advanced theory without application.
4. Find a mentor and a community by looking for people already working in ML through school, LinkedIn, open-source projects, or forums. A mentor can save you years of trial and error.
5. CS major is a great choice. Over time, your strengths and interests will naturally guide you toward a specific ML niche.
Good luck on your journey!
– Siva
It’s great that you are thinking about this early — that already puts you ahead. You are right about focusing on math and Python, which is critical and I want to emphasize something important: learning ML is not about going through tutorials; it’s about solving real problems.
Here are some practical approaches to your ML journey:
1. Pick a real-world problem you actually care about. It could be in health, sports, climate, finance, education or anything that motivates you. Because, ML takes time and patience. Passion matters.
2. Use AI as your learning partner to help design the solution, explain algorithms, and review your code. But make sure you understand why things work. Your goal is to become a strong problem solver, not a model user.
3. Build small, end-to-end projects. Even simple projects teach more than advanced theory without application.
4. Find a mentor and a community by looking for people already working in ML through school, LinkedIn, open-source projects, or forums. A mentor can save you years of trial and error.
5. CS major is a great choice. Over time, your strengths and interests will naturally guide you toward a specific ML niche.
Good luck on your journey!
– Siva
Chinyere Okafor
Educationist and Counseling Psychologist
1185
Answers
Updated
Chinyere’s Answer
Hi Dani,
First of all, it's good that you are already considering your future with such clarity. You have a great starting point if you had an interest in AI prior to attending college and were already enrolled in Java and math classes. You don't have to be an expert yet, but great machine learning engineers usually possess curiosity and drive.
You are entirely correct that the fundamental building blocks of machine learning are arithmetic and Python. ML involves a lot of linear algebra, probability, statistics, and calculus; therefore, it will be very beneficial to keep getting comfortable with these concepts. Since Python is the primary language used in the sector, mastering it is a wise decision, particularly for data manipulation and modeling. The good news is that you don't have to become an expert at everything at once. Speed is not nearly as important as solid fundamentals.
Prioritize developing a strong foundation in computer science as you transition into a UW computer science major. Although they might not initially seem "AI-heavy," courses in databases, systems, algorithms, and data structures are important. Machine learning engineers are expected to do more than just train models; they must also design clear, effective code and understand how software functions at scale.
This is the opportunity to truly differentiate yourself outside of the classroom. As early as you can, try out tiny, practical projects. For instance, you could create a basic picture classifier, a recommendation system, or a sentiment analysis model for text. These don't have to be revolutionary; what counts is that you understand what you're creating and are able to communicate your ideas. These projects have the potential to develop into a portfolio over time that demonstrates actual skill rather than just grades.
Learning standard machine learning tools and concepts, including NumPy, pandas, scikit-learn, and eventually PyTorch or TensorFlow, is also useful. No hurry, once again. AI appears enormous from the outside, but when you understand it layer by layer, it becomes manageable, which makes many students feel overwhelmed.
Getting used to the mindset of learning that is necessary in the tech industry is another key stage. A lot of trial and error is involved in machine learning; models fail, code fails, and initial findings don't make sense. That's typical. Just as important as intelligence is the capacity for patience, troubleshooting, and lifelong learning.
Explore research opportunities, internships, hackathons, or AI groups when you start college. Early on, even unpaid research or short projects with professors can be quite beneficial. These encounters provide you with a better idea of what actual ML work entails and whether you find it enjoyable on a daily basis.
There isn't a single "perfect path" to becoming a machine learning engineer, which is something I want to stress. While some are from data science, math, or engineering, others are from computer science. They are similar in that they have solid foundations, regular practice, and curiosity.
Right now, you're asking questions, developing skills, and becoming ready ahead of time. Don't rush; continue to concentrate on studying. You'll be in a great position for an MLE career if you maintain your consistency throughout college, particularly with projects and internships.
Best wishes!
First of all, it's good that you are already considering your future with such clarity. You have a great starting point if you had an interest in AI prior to attending college and were already enrolled in Java and math classes. You don't have to be an expert yet, but great machine learning engineers usually possess curiosity and drive.
You are entirely correct that the fundamental building blocks of machine learning are arithmetic and Python. ML involves a lot of linear algebra, probability, statistics, and calculus; therefore, it will be very beneficial to keep getting comfortable with these concepts. Since Python is the primary language used in the sector, mastering it is a wise decision, particularly for data manipulation and modeling. The good news is that you don't have to become an expert at everything at once. Speed is not nearly as important as solid fundamentals.
Prioritize developing a strong foundation in computer science as you transition into a UW computer science major. Although they might not initially seem "AI-heavy," courses in databases, systems, algorithms, and data structures are important. Machine learning engineers are expected to do more than just train models; they must also design clear, effective code and understand how software functions at scale.
This is the opportunity to truly differentiate yourself outside of the classroom. As early as you can, try out tiny, practical projects. For instance, you could create a basic picture classifier, a recommendation system, or a sentiment analysis model for text. These don't have to be revolutionary; what counts is that you understand what you're creating and are able to communicate your ideas. These projects have the potential to develop into a portfolio over time that demonstrates actual skill rather than just grades.
Learning standard machine learning tools and concepts, including NumPy, pandas, scikit-learn, and eventually PyTorch or TensorFlow, is also useful. No hurry, once again. AI appears enormous from the outside, but when you understand it layer by layer, it becomes manageable, which makes many students feel overwhelmed.
Getting used to the mindset of learning that is necessary in the tech industry is another key stage. A lot of trial and error is involved in machine learning; models fail, code fails, and initial findings don't make sense. That's typical. Just as important as intelligence is the capacity for patience, troubleshooting, and lifelong learning.
Explore research opportunities, internships, hackathons, or AI groups when you start college. Early on, even unpaid research or short projects with professors can be quite beneficial. These encounters provide you with a better idea of what actual ML work entails and whether you find it enjoyable on a daily basis.
There isn't a single "perfect path" to becoming a machine learning engineer, which is something I want to stress. While some are from data science, math, or engineering, others are from computer science. They are similar in that they have solid foundations, regular practice, and curiosity.
Right now, you're asking questions, developing skills, and becoming ready ahead of time. Don't rush; continue to concentrate on studying. You'll be in a great position for an MLE career if you maintain your consistency throughout college, particularly with projects and internships.
Best wishes!
Hello Chinyere! I really appreciate the comment, thank you a lot for your advice!
Dani
You're welcome, Dani!
Chinyere Okafor
Teklemuz Ayenew Tesfay
Electrical Engineer, Software Developer, and Career Mentor
517
Answers
Updated
Teklemuz Ayenew’s Answer
Start by building a strong base in math and learn Python along with important libraries like NumPy, Pandas, Scikit-learn, and TensorFlow or PyTorch. Take relevant courses in DSA, databases, and machine learning through your university or online platforms like Coursera, edX, Udacity, or YouTube. Gain hands-on experience with personal projects, volunteering, or free platforms like Kaggle, DrivenData, Zindi, and Google Colab to work on real datasets and competitions. Join AI and machine learning communities such as ACM Student Chapters, university AI clubs, and online forums like Stack Exchange, AIcrowd, and OpenML, and participate in discussions on Discord and Slack. Attend conferences, hackathons, meetups, and career fairs, and take part in online ML competitions on platforms like Kaggle, Devpost, MachineHack, HackerEarth, and AIcrowd to practice skills, build portfolio pieces, and network. Share your work on GitHub or a personal portfolio to showcase your skills.
Look for free virtual internships and structured programs such as Whispry Free Virtual Internships, AICTE-approved online internships, and the IBM SkillsBuild AI Internship, which provide project-based learning, mentorship, and certificates at no cost. Seek guidance from professors, senior students, or industry professionals. Contribute to open-source projects, join coding reviews, and collaborate with others. As you grow, explore deep learning, NLP, and computer vision, read research papers, and replicate experiments to deepen understanding while networking with professionals, alumni, and community members. By following these steps, you'll be well-prepared and competitive as a Machine Learning Engineer.
Look for free virtual internships and structured programs such as Whispry Free Virtual Internships, AICTE-approved online internships, and the IBM SkillsBuild AI Internship, which provide project-based learning, mentorship, and certificates at no cost. Seek guidance from professors, senior students, or industry professionals. Contribute to open-source projects, join coding reviews, and collaborate with others. As you grow, explore deep learning, NLP, and computer vision, read research papers, and replicate experiments to deepen understanding while networking with professionals, alumni, and community members. By following these steps, you'll be well-prepared and competitive as a Machine Learning Engineer.
Thank you Teklemuz for your comment, I will definitely use your comment as a guide for my career!
Dani
You're welcome!
Teklemuz Ayenew Tesfay
Updated
Kirthi’s Answer
Hi Daniel! I am signing off from Verizon India, but I couldn't leave without answering this. It is thrilling to see a high school senior already tackling college-level CS—that head start is your biggest asset! Here are 5 creative steps to turn that passion into a career:
Treat Math like a Superpower, not a chore: Don't just pass your Calculus and Linear Algebra classes; try to visualize them. These aren't just equations; they are the actual "engine" of AI. If you understand the math deeply, you won't just be a user of AI libraries—you will be an innovator who can build them.
Become a "Data Detective": Real-world Machine Learning isn't just about cool algorithms; it is 80% dealing with messy, chaotic data. Learn to love the "grunt work" of cleaning and analyzing datasets. If you can find the story hidden inside raw data, you will be unstoppable.
Build "Useless" Things: Stop watching tutorials and start building projects that actually interest you, even if they seem silly. Build an AI that predicts if it will rain in Seattle or a model that recognizes your handwriting. You learn more from one project that breaks than from ten tutorials that work perfectly.
Find Your Tribe at UW: You are heading to a world-class university—use it! Don't just sit in the back of the class. Join the AI clubs, go to hackathons, and talk to your professors after hours. Innovation happens in conversations, not just in isolation.
Stay "Forever Junior": AI changes at light speed. The tools we use today might be gone in five years. The most important skill you can learn is how to learn. Stay humble, stay curious, and never stop being a student of the game.
Go change the world, Daniel! Best of Luck!
Treat Math like a Superpower, not a chore: Don't just pass your Calculus and Linear Algebra classes; try to visualize them. These aren't just equations; they are the actual "engine" of AI. If you understand the math deeply, you won't just be a user of AI libraries—you will be an innovator who can build them.
Become a "Data Detective": Real-world Machine Learning isn't just about cool algorithms; it is 80% dealing with messy, chaotic data. Learn to love the "grunt work" of cleaning and analyzing datasets. If you can find the story hidden inside raw data, you will be unstoppable.
Build "Useless" Things: Stop watching tutorials and start building projects that actually interest you, even if they seem silly. Build an AI that predicts if it will rain in Seattle or a model that recognizes your handwriting. You learn more from one project that breaks than from ten tutorials that work perfectly.
Find Your Tribe at UW: You are heading to a world-class university—use it! Don't just sit in the back of the class. Join the AI clubs, go to hackathons, and talk to your professors after hours. Innovation happens in conversations, not just in isolation.
Stay "Forever Junior": AI changes at light speed. The tools we use today might be gone in five years. The most important skill you can learn is how to learn. Stay humble, stay curious, and never stop being a student of the game.
Go change the world, Daniel! Best of Luck!
Hello Kirthi, thank you a lot for taking the time to write your comment :) I appreciate it!
Dani
Updated
Sumitra’s Answer
Hello Daniel,
I love that you are thinking about this in high school. You are already doing the right things (CS + math + coding), so now it’s mostly about being consistent and building proof of work. Keep getting really comfortable with Python, and don’t stress about “Learning all of AI” at once. Start with the basics of machine learning (predicting/ recognizing patterns) and practice on a few small datasets so you understand what helps a model do better and what makes it fail. Alongside that, make a simple portfolio comprising 3–5 projects you are proud of, where you explain what you built, what you learned, and what you would improve next (GitHub is perfect for this). In college, prioritize strong CS foundations (data structures/algorithms) and stats/probability, then take ML/AI electives once those feel solid. If you do these step by step, you will be in a great spot for internships and MLE roles later, and you will actually understand what you are doing, not just follow tutorials.
Hope this helps!☺️ All the best for your future endeavours!
I love that you are thinking about this in high school. You are already doing the right things (CS + math + coding), so now it’s mostly about being consistent and building proof of work. Keep getting really comfortable with Python, and don’t stress about “Learning all of AI” at once. Start with the basics of machine learning (predicting/ recognizing patterns) and practice on a few small datasets so you understand what helps a model do better and what makes it fail. Alongside that, make a simple portfolio comprising 3–5 projects you are proud of, where you explain what you built, what you learned, and what you would improve next (GitHub is perfect for this). In college, prioritize strong CS foundations (data structures/algorithms) and stats/probability, then take ML/AI electives once those feel solid. If you do these step by step, you will be in a great spot for internships and MLE roles later, and you will actually understand what you are doing, not just follow tutorials.
Hope this helps!☺️ All the best for your future endeavours!
Hello Sumitra! thank you for your comment. I really appreciate it!
Dani
Updated
Ankita’s Answer
It's great that you're already thinking about this in high school! You're off to a strong start with computer science, math, and coding. Focus on being consistent and showing your skills. Treat Python as your main tool—master it first without worrying about learning everything in AI at once. Begin with simple machine learning tasks, like predicting patterns or recognizing trends. Use small datasets to figure out what makes a model work well or fail.
Create a portfolio of 3 to 5 projects you're proud of. For each project, explain what you built, what you learned, and what you could improve. Use GitHub to showcase your work. When you get to college, strengthen your core skills in data structures, algorithms, and statistics. Once you're comfortable, take ML/AI electives. Follow these steps, and you'll be ready for internships and future roles in machine learning engineering. You'll also truly understand what you're doing, not just follow tutorials.
Think of each project as a step in building your own success story.
Create a portfolio of 3 to 5 projects you're proud of. For each project, explain what you built, what you learned, and what you could improve. Use GitHub to showcase your work. When you get to college, strengthen your core skills in data structures, algorithms, and statistics. Once you're comfortable, take ML/AI electives. Follow these steps, and you'll be ready for internships and future roles in machine learning engineering. You'll also truly understand what you're doing, not just follow tutorials.
Think of each project as a step in building your own success story.
Thank you a lot Ankita Jain. I really appreciate you taking your time for responding!
Dani
Updated
Ponnu’s Answer
Hi Dani,
To become a Machine Learning Engineer, start by building strong foundations in math (especially linear algebra, statistics, and calculus) and programming, with Python as the primary language. Learn core machine learning concepts and algorithms, then apply your knowledge by building real-world projects and sharing your work on platforms like GitHub. Develop software engineering skills, such as version control and model deployment, and deepen your understanding with advanced topics like deep learning and MLOps. Finally, engage with the ML community, participate in competitions, and apply for internships or entry-level roles to gain practical experience and grow your professional network.
To become a Machine Learning Engineer, start by building strong foundations in math (especially linear algebra, statistics, and calculus) and programming, with Python as the primary language. Learn core machine learning concepts and algorithms, then apply your knowledge by building real-world projects and sharing your work on platforms like GitHub. Develop software engineering skills, such as version control and model deployment, and deepen your understanding with advanced topics like deep learning and MLOps. Finally, engage with the ML community, participate in competitions, and apply for internships or entry-level roles to gain practical experience and grow your professional network.
Hello Ponnu, thank you a lot for your advice. I appreciate it :)
Dani
Updated
Latikesh’s Answer
1. One thing I really want to emphasise early, because it trips a lot of people up: don’t try to learn everything in AI. AI is massive, and it’s easy to get overwhelmed. There are different paths like data science, AI/ML engineering, Ops, etc. Try to narrow your scope over time.
2. A lot of students underestimate how important core CS is — but in the real world, MLEs are engineers first. So focus on building strong foundations.
3. Also, don’t stress too much about landing a perfect “ML internship”. Early on, any software, data, or engineering experience is valuable. A lot of people become MLEs by first working as software engineers and gradually moving closer to ML — that’s very normal.
I guess the other points are well covered by rest of the folks here.
2. A lot of students underestimate how important core CS is — but in the real world, MLEs are engineers first. So focus on building strong foundations.
3. Also, don’t stress too much about landing a perfect “ML internship”. Early on, any software, data, or engineering experience is valuable. A lot of people become MLEs by first working as software engineers and gradually moving closer to ML — that’s very normal.
I guess the other points are well covered by rest of the folks here.
Hello Latikesh, I greatly appreciate your response to my question :)
Dani
Updated
Roel’s Answer
You’re on the right path, Daniel! Here’s a simple guide to becoming a Machine Learning Engineer:
Study Computer Science: Enroll in computer science at UW and focus on important courses like algorithms, data structures, and software engineering.
Boost Your Math Skills: Make sure to learn calculus, linear algebra, probability, and statistics since they are key to understanding machine learning.
Get Good at Python: Keep improving your Python skills, as it’s the main language used in machine learning.
Learn the Basics of ML: Take electives or online courses in machine learning, artificial intelligence, and data science to build a strong foundation.
Work on Projects: Use what you’ve learned by creating ML projects. Find datasets on sites like Kaggle or UCI and share your projects on GitHub.
Gain Experience: Look for internships or research opportunities in AI/ML to get hands-on experience.
Stay Updated: Follow AI research, attend workshops, and join ML communities to keep learning.
With your background and plans to major in CS, you’re doing great! Keep improving your math and coding skills, and start applying them to real-world ML challenges.
Study Computer Science: Enroll in computer science at UW and focus on important courses like algorithms, data structures, and software engineering.
Boost Your Math Skills: Make sure to learn calculus, linear algebra, probability, and statistics since they are key to understanding machine learning.
Get Good at Python: Keep improving your Python skills, as it’s the main language used in machine learning.
Learn the Basics of ML: Take electives or online courses in machine learning, artificial intelligence, and data science to build a strong foundation.
Work on Projects: Use what you’ve learned by creating ML projects. Find datasets on sites like Kaggle or UCI and share your projects on GitHub.
Gain Experience: Look for internships or research opportunities in AI/ML to get hands-on experience.
Stay Updated: Follow AI research, attend workshops, and join ML communities to keep learning.
With your background and plans to major in CS, you’re doing great! Keep improving your math and coding skills, and start applying them to real-world ML challenges.
Hello Roel, thank you very much for your advice!
Dani