How to Become a Machine Learning Engineer

As AI continues to penetrate all areas of our lives, its subsets are becoming increasingly popular as well. Machine learning (ML) is an AI subset that enables computers to learn and improve without explicit programming. Machine learning teaches algorithms to analyze data and find hidden patterns, allowing machines to make decisions or predictions involving new, as yet unseen data.

This surge in AI/ML popularity has created greater demand for careers in these fields. This article focuses on how to become a machine learning engineer, covering the needed skills, what the position entails, a typical learning path, and how to get your first job as a machine learning engineer.

But let’s start with the basics. What is a machine learning engineer?

What Is a Machine Learning Engineer

Machine learning engineering is a subfield of software engineering. Machine learning engineers create programs that equip computers with the resources they need to self-learn.

Their objective is to transform data into a usable product, meaning that these engineers could be described as technically sound programmers who research, build, and design self-learning software to automate predictive models.

Like their software engineering counterparts, machine learning engineers need to be skilled programmers who routinely work with software engineering tools such as Docker, IDEs, and GitHub. Machine learning engineers set themselves apart from software engineers by combining their software engineering knowledge with machine learning expertise.

Let’s take a closer look at what a machine learning engineer does.

What Does a Machine Learning Engineer Do

Machine learning engineers are valuable members of any organization’s data science team—they research, design, and build AI systems that manage machine learning operations and improve and maintain existing AI systems.

Machine learning engineers often serve as essential communicators among other data science team members, typically working directly with the data scientists who develop AI system-building models and with the people who build and run them.

Machine learning engineers’ job responsibilities may vary by organization, but they often cover:

• Designing and building machine learning systems and frameworks

• Researching, designing, and building scalable machine learning pipelines that automate the machine learning workflow

• Deploying and monitoring machine learning models, retraining them when necessary

• Machine learning algorithm implementation

• Running AI system tests and experiments

• Conducting statistical analyses

• Sourcing and pulling datasets necessary for dealing with current problems, typically working alongside data engineers in collaboration

• Verifying that the extracted data is good and cleaning it when needed

• Managing the infrastructure needed to take a model into production

Skills Needed for a Machine Learning Engineer

As can be inferred from the above section, organizations expect much from machine learning engineers. Consequently, a good machine learning engineer needs the proper set of skills to meet the challenges of their position. We’re breaking down these requirements into technical “hard” skills and less tangible “soft” skills.

Technical/Hard Skills

• Advanced Programming: Let’s start with the most basic skill needed—writing code. Although most machine learning professionals use Python and R, some organizations may require knowledge in other languages, such as C++ or Java.

• Machine Learning Algorithms and Frameworks: There’s a robust selection of machine learning frameworks, including Hugging Face, PyTorch, scikit-learn, and TensorFlow. All of these frameworks make machine learning accessible; choosing and optimizing the most fitting model for the tasks at hand requires a solid working knowledge of machine learning algorithms, their hyperparameters, and how they can impact learning. Machine learning engineers also require a good understanding of the inner workings of different machine learning algorithms, as they must be aware of the advantages and disadvantages of each approach when solving a problem.

• MLOps: Machine learning operations (or MLOps for short) is one of machine learning engineering’s primary functions. MLOps focuses on streamlining the deployment of machine learning models to production and on the resources needed to maintain and monitor them once in production.

• Mathematics, Probability, and Statistics: These disciplines play a vital role in the machine learning process. For example, linear algebra (a subfield of mathematics) heavily involves matrices, vectors, and linear transformations, all of which are critical foundational aspects of machine learning. Other essential techniques require a good understanding of probability to handle real-world uncertainty more effectively and of statistics to create and validate machine learning models.

• Software Engineering and System Design: Functional software is the final deliverable for machine learning engineers. This means that careful consideration of system design must be factored into the development of a machine learning system that scales well as data increases. Additionally, machine learning systems are only a minor component of a larger system, so machine learning engineers must understand software engineering best practices such as documentation, modular coding, version control, testing, etc., as well as how the different pieces fit together to form a system.

Soft Skills

• Communication: Machine learning engineers inevitably must work with different stakeholders. Although some stakeholders, such as data scientists, may be technically proficient, others, such as product teams, may not be. Consequently, machine learning engineers must know how to adapt their communication style to suit any stakeholders effectively.

• Continuous Learning: The machine learning field is continuously growing and evolving. Consequently, machine learning engineers must stay current regarding new tools and processes, how they work, where they are best used, and where they don't work. Thus, becoming a machine learning engineer means embarking on a never-ending learning journey.

• Problem-Solving: Set aside the fancy machine learning tools, and you’re left with the main machine learning objective: solving a problem. So, machine learning engineers who want to solve problems effectively must think creatively and critically.

Typical Machine Learning Engineer Learning Path

Now that we better understand the skill requirements for a machine learning engineer, let’s look at an effective way to develop those skills.

• Earn the right bachelor’s degree: Machine learning is part of computer science, so you need a solid background in computer programming, data science, and mathematics. Most ML engineering jobs require at least a bachelor's degree, so begin by studying computer science or a similar field, such as statistics. If you don’t have a bachelor’s, don’t worry; there are other ways.

• Learn coding with Python: Python and C++ are among the most widely used programming languages for machine learning engineers and data scientists alike. Also, become familiar with SQL and GitHub to access organizational data better and work collaboratively with a team.

• Get familiar with machine learning concepts: Learn various ML algorithms, including linear regression, neural networks, and support vector machines. Acquire hands-on experience with popular machine learning frameworks like PyTorch, TensorFlow, and scikit-learn. Finally, practice by building and experimenting with models on platforms like DataLab, Kaggle, and Google Colab.

• Take machine learning courses: There is a wide selection of machine learning certification courses available, both online and onsite. These courses allow students to acquire a practical understanding of machine learning in a relatively short period of time. In some instances, the right certification course could potentially make up for some gaps in your bachelor’s degree.

• Experiment with machine learning projects: Try reviewing and recreating basic projects found in Awesome Machine Learning, scikit-learn, PredictionIO, and related resources. Once you have a good understanding of how machine learning works in practice, try creating your own projects that you can eventually use in a portfolio, share online, or list on your resume or CV.

• Network and build bridges: Connections can help smooth the path for any job seeker, including those seeking machine learning engineer positions. For example, Kaggle is an online community used by machine learning professionals and data scientists. The platform lets users find and publish data sets, construct models in a web-based data science environment, and interact with other machine learning engineers.

• Apply for a machine learning engineer internship or job: Consider an internship or entry-level position in machine learning. These positions impart experience, skill learning, and potential employment contacts.

How To Get Your First Job as a Machine Learning Engineer

So, you’ve acquired the proper education, skills, and certifications. Now, how do you go about securing that initial position?

1. Build your portfolio

Work on projects that show off your skills and help you construct a portfolio. These projects could be a series of well-written blog posts that demonstrate how to implement a given tool or approach a particular problem. A project could also be an end-to-end system you created to predict the different outcomes you get when using different inputs. Since there are no universally accepted criteria for machine learning engineers yet, hiring managers must rely on a strong portfolio to demonstrate the candidate's necessary skills.

2. Reach out

There is no single right way to do outreach. You may visit various job boards or use job recruitment sites such as LinkedIn. However, a more strategic approach is to identify a set of companies you’d like to be your employers.

Once you have compiled your list of ideal companies, reach out to decision-makers, such as hiring managers, chief data scientists, or team leads, at these organizations via social media platforms like LinkedIn and Twitter.

How To Prepare for Your Machine Learning Engineer’s Interview

Each company has its preferred way of conducting its interview process, so finding each organization’s approach can be a daunting task. Before your first interview, try asking how the organization’s interview process works, even though this information is typically provided to you in advance. Also, many companies take their approach from big IT corporations like Apple, Facebook, or Google, then adjust it to suit their specific needs.

Consider taking advantage of the many online sites that post the most popular machine learning engineer interview questions. Many of these “cheat sheets” demystify the interview process and help you prepare for the kinds of questions you’ll be asked.

And finally, research the company you are interviewing with. Find out what they do, when they were founded, what their corporate philosophy is, how big they are, and what industry they belong to. Interviewers love it when candidates care enough to take the time to familiarize themselves with the organization they’re applying to.

Machine Learning Engineer Salary and Industry Trends

Machine learning engineers are among the highest-paid professionals in the tech industry. According to compensation data from Indeed, machine learning engineers in the U.S. earn an average salary of around $186,000 per year (as of March 2026). However, actual compensation can vary based on experience level, geographic location, industry, and technical specialization.

Machine learning engineers continue to be in demand due to several industry trends, including:

• Increased implementation of AI across healthcare, finance, retail, and technology sectors

• Increased focus on deploying, monitoring, and maintaining machine learning models in real-world environments

• Growing need for engineers who can bridge data science and software engineering teams

How To Get a Head-Start on Your Machine Learning Engineer Journey

If this vast amount of information and advice seems daunting, don’t worry. The more you understand the field of machine learning, the less intimidating it will be.

This intensive 26-week AI & machine learning live online bootcamp covers both theoretical knowledge and practical skill-building, including popular frameworks and tools such as Python, Pandas, AWS SageMaker, scikit-learn, TensorFlow, NLTK, and more, preparing you for roles such as ML Engineer, AI Engineer, Data Scientist, and Analyst.

FAQs

What does it take to be a machine learning engineer, or ML engineer?

Machine learning engineer requirements revolve around advanced math/stats (calculus, linear algebra, and probability), strong computer science fundamentals, and expert programming (R and Python) with frameworks (PyTorch and TensorFlow). Machine learning engineers need essential skills, including data preprocessing, building and deploying models, system design, understanding algorithms, and practical experience gained through projects and advanced degrees (Master's/PhD) in computer science or related fields, as well as soft skills such as problem-solving and communication.

What are the qualifications for a machine learning engineer?
There is no single required qualification to become a machine learning engineer, but most employers expect a strong background in computer science, mathematics, and programming. Many candidates hold a bachelor’s degree in fields such as computer science, data science, software engineering, statistics, or a related discipline, while some advanced roles may prefer a master’s degree or a PhD.

That said, formal degrees are not the only pathway. Many aspiring machine learning engineers build the required skills through certifications, hands-on projects, and structured training programs and bootcamps, which provide practical experience with machine learning tools, frameworks, and real-world applications that employers value.

Can a beginner become a machine learning engineer?

Yes, a beginner can become a machine learning engineer. Still, it's challenging and demands robust foundational skills, solid project experience, and a focus on specific concepts, such as the Python programming language, mathematics (especially stats/linear algebra), and software engineering principles. Typically, freshers start with roles such as ML intern or data scientist to gain experience before attempting a dedicated ML engineer position. Although some entry-level machine learning engineer roles exist, many recruiters prefer experience and skills. Thus, mastering core machine learning concepts and building a strong portfolio is crucial for newbies.