How to Become a Machine Learning Engineer in 2024

Machine learning (ML) is a subfield of artificial intelligence (AI) and computer science that focuses on imitating how humans learn by leveraging data and algorithms. The main objective of machine learning is to identify patterns in data.

It's widely believed that AI will continue to transform business as we know it, and this revolution is well underway across various industries. Consequently, companies are investing heavily in the field. As of mid-2023, the average deal size for AI companies reached $29 million, reflecting a nearly 50% increase compared to 2022’s full-year total. This surge is partly driven by the boom in generative AI.

One such role that is necessary for this push is that of the machine learning engineer.  There are several compelling reasons to want to become a machine learning engineer:

• It’s a lucrative career option.
• It’s an exciting field that will always present new challenges and require continual learning.
• A career in artificial intelligence puts you at the center of the most cutting-edge technological game-changer in modern industry. In a separate article, you can explore AI vs ML in more detail and learn more about AI engineer skills in our dedicated post.  

Now that we’ve established the “why” of becoming a machine learning engineer, we will explain what a machine learning engineer does and how you can become one. 

What is a Machine Learning Engineer?

Machine learning engineering is considered a subfield of software engineering, so it’s fair to say their lifestyles are quite similar. Like software engineers, employers expect machine learning engineers to be proficient programmers familiar with software engineering tools such as IDEs, GitHub, and Docker. 

The main difference is that machine learning engineers focus on creating programs that provide computers with the necessary resources to self-learn. They make this distinction by combining their knowledge of software engineering with that of machine learning. 

A machine learning engineer’s objective is to convert data into a product. Thus, a machine learning engineer may be described as a technically sound programmer researching, building, and designing self-learning software to automate predictive models. 

What Does a Machine Learning Engineer Do?

Everyone would have heard of the data scientists - especially after Havard Business School called it the sexiest role of the 21st century. Compared to data scientists, machine learning engineers appear slightly further down the line within a project. To put it into perspective, a data scientist would analyze data to generate business insights, whereas a machine learning engineer would turn the data into a product. 

A machine learning engineer would be much more focused on writing code that takes theoretical data science models and scales them to the production level for deployment as a machine learning product. However, the specifics of a machine learning engineer's responsibilities may change depending on two key factors: 1) the organization's size and 2) the type of project. 

There are still some general responsibilities that you can expect from a role as a machine learning engineer. These responsibilities include: 

• Designing, researching, and developing scalable machine learning pipelines that automate the machine learning workflow
• Scaling data science prototypes 
• Sourcing and extracting datasets that are appropriate to tackle the problem at hand. This may be done in collaboration with data engineers 
• Verifying the data they’ve extracted is of good quality and cleaning it
• Leveraging statistical analysis to improve the quality of machine learning models 
• Building data and model pipelines
• Managing the infrastructure required to take a model to production
• Deploying machine learning models
• Monitoring machine learning systems in production and retraining them when it’s necessary 
• Building machine learning frameworks 

Chip Huyen, a writer and prominent figure in machine learning, suggested that it’s a good practice not to get hung up on the role definitions since they typically serve as an inaccurate reflection of what you may be doing. For instance, it’s possible to come across two people working on the same team who perform significantly different tasks. Still, you may also come across two people at different companies that do similar things but have very different titles.

Machine learning engineering compared to other similar data roles

What Skills Does a Machine Learning Engineer Need? 

Machine learning engineers sit at the intersection of software engineers and data scientists. Due to its interdisciplinary nature, you’ll have to be well-versed in foundational data science skills and have a solid grasp of software engineering principles. 

It’s important to note that most machine learning engineer roles do not require a degree, despite several job descriptions still listing it as a requirement. If you can demonstrate the necessary skills required of a machine learning engineer in your portfolio, you can still be considered. Let’s dive deeper into the education, skills, and experience needed to give you a better idea of what you’d need to demonstrate. 

Technical skills

• Advanced programming: The most obvious requirement is the ability to write code. Python and R are the most popular languages for machine learning practitioners. However, some companies may require you to know other languages like C++ and Java. 
• Mathematics, probability, and statistics: Mathematics, probability, and statistics play a significant role in machine learning. For example, linear algebra (a sub-field of mathematics) focuses heavily on vectors, matrices, and linear transformations, which are all critical foundational aspects of machine learning. We often see it in notations describing how an algorithm works and must have good knowledge of it when implementing an algorithm in code. Other vital techniques require a good understanding of probability to help us deal with uncertainty in the real world, as well as statistics to help us build and validate our models. 
• Machine learning algorithms and frameworks: It’s doubtful that you’ll have to implement a machine learning algorithm from scratch. Several knowledgeable people have created various machine learning frameworks (i.e., scikit-learn, TensorFlow, PyTorch, Hugging Face, etc.) that make machine learning accessible. However, choosing and optimizing a suitable model for the task requires good knowledge of machine learning algorithms, their hyperparameters, and how their hyperparameters impact learning. You must also be aware of the pros and cons of taking each relative approach when solving a problem, which also requires good knowledge of the inner workings of various machine learning algorithms.
• Software engineering and system design: The final deliverable for a machine learning engineer is workable software. Careful thought about how the system is designed must go into developing machine learning systems to scale well with increasing data. Also, a machine learning system is a minor component required to fit into a more extensive system. Thus, a machine learning engineer must understand various software engineering best practices (i.e., version control, testing, documentation, modular coding, etc.) and how the different pieces form a system. You’ll be required to build an appropriate interface for your machine learning model that can effectively communicate with system components.
• MLOps: Machine learning operations (MLOps) is one of the core functions of machine learning engineering. It focuses on streamlining the process of deploying machine learning models to production and the necessary resources to maintain and monitor them once in production. It’s still a reasonably new function, but it’s beginning to gain traction as a practical approach for creating high-quality machine learning applications. 

Soft skills 

• Communication: Machine learning engineers must work with various stakeholders. Some of these stakeholders will be pretty technical (e.g., data scientists), while others may not be (e.g., product teams). Thus, effectively adapting your communication style for your stakeholders is vital. 
• Problem-solving: Despite all of the fancy tools at the forefront of machine learning, the main objective of a machine learning project is to solve a problem. This means thinking creatively and critically about problems is a highly desirable trait for machine learning engineers. 
• Continuous learning: Machine learning is a rapidly evolving field. As you read this article, you see that a researcher is working somewhere on improving some model or process. To remain at the cutting edge, you must have a knack for rapidly learning new tools, how they work, where they work well, and where they don’t. In short, the decision to be a machine learning engineer is an implicit commitment to continual learning.

The Machine Learning Engineer Learning Path

Embarking on the journey to become a machine learning engineer can be both exciting and challenging. As we saw before, the field requires a blend of theoretical knowledge and practical skills. Here’s a structured learning path to guide you through acquiring the necessary expertise:

1. Build a Strong Foundation

Start with the basics of linear algebra, calculus, probability, and statistics. These are important for understanding machine learning algorithms and models. Then, become proficient in programming languages commonly used in machine learning, such as Python and R. Learn the fundamentals of coding, data structures, and algorithms.

2. Dive into Machine Learning Concepts

Familiarize yourself with various machine learning algorithms, including linear regression, decision trees, support vector machines, and neural networks. Understanding how these algorithms work and when to use them is essential.

Gain hands-on experience with popular machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. These tools simplify the implementation of complex algorithms and models. Practice by building and experimenting with models on platforms like Kaggle, Google Colab, and DataLab.

3. Develop Software Engineering Skills

Learn the principles of designing scalable and efficient systems. This includes understanding APIs, microservices, and cloud computing. Resources like "Designing Data-Intensive Applications" by Martin Kleppmann provide in-depth knowledge of system design.

Master version control systems like Git and platforms like GitHub. These are essential for collaboration and managing codebases effectively. Participate in open-source projects to gain real-world experience.

4. Explore MLOps

Understand the processes involved in deploying machine learning models to production. Learn about Docker, Kubernetes, and cloud platforms like AWS, Google Cloud, and Azure. These skills are crucial for ensuring your models are accessible and maintainable.

Learn how to monitor your models' performance in production and implement strategies for retraining and updating them as new data becomes available. This includes setting up pipelines and automated workflows.

5. Build a Portfolio

Work on real-world projects that showcase your skills. These can range from simple data analysis tasks to complex machine learning applications. Document your projects thoroughly and publish them on platforms like GitHub or personal blogs.

Participate in machine learning competitions on platforms like Kaggle and DataCamp. These competitions provide practical experience and help you apply your knowledge to solve real-world problems.

Learning Path Milestones

How to Land Your First Machine Learning Engineering Job

How do you land your first job? This can be broken into two phases: 1) Portfolio building and 2) Outreach. The portfolio-building phase should occur while you’re learning machine learning. A portion of outreach should happen, but it accelerates when you’ve got a strong portfolio. So let’s dive deeper into each phase. 

The portfolio-building phase 

One of the most demanding challenges in applying for machine learning roles is landing an interview. Since the field is relatively new, there are no universal validating criteria that companies use to decipher if a candidate is a suitable fit for a machine learning engineer’s role. Of course, it doesn’t help that most job openings receive hundreds of applications per day.

To offset the backlog, candidates' resumes are often passed through an ATS system that filters applications by specific keywords. Unfortunately, people quickly caught on and filled their resumes with keywords to beat ATS systems. So how can you ensure companies notice you? 

One solution is to work on projects that demonstrate your skills and help you build a portfolio. These projects may be several well-crafted blog posts that detail an approach to a problem or how to implement a particular tool (i.e., setting up monitoring for a production-ready machine learning model). A project may also be an end-to-end system you’ve designed to predict an outcome given some inputs. What matters most is that you can demonstrate the capabilities that employers want. 

If you’re unsure what project to build, you could participate in data science competitions hosted on platforms like DataCamp and Kaggle. Contributing to such competitions is highly regarded among many employers and is a great way to build a portfolio.

You can get an idea of what it is like to participate in a competition with this Kaggle Competition Tutorial. 

The outreach phase

Once you have a portfolio that speaks for you, the next stage is outreach. Several people prefer the traditional way of job seeking, which involves using job boards to apply for as many jobs as possible with the same resume. While this may lead to some success, it’s more of a brute-force method. 

A more strategic approach to landing a job is to lay out a set of companies you’d like to be your employer. For example, would you prefer a company using machine learning or a company that enhances current systems? What size would you like your ideal company to be? Begin to ask yourself questions like these to break down what your ideal employer looks like and list them out.

Once you have a list of ideal companies, you can seek out decision-makers (i.e., hiring managers, chief data scientists, team leads) at these organizations using social media platforms like LinkedIn and Twitter. Try to attach a friendly message to add value to them since it’s highly likely they already receive tons of messages from people seeking opportunities. Coming from a giving perspective is more likely to get them interested. 

Hi [Insert name],
I read the system design article for your recommendation system and admire how you dealt with the cold-start problem. Given your team's high level of expertise, you’ve probably thought of this already: recommending popular articles is extremely useful for aiding people’s decisions. I conducted a project to approach that problem - here’s the link [insert link]. Would you be available for a short chat about the approach I took on this project? Please let me know your preferred time to speak. Here’s my availability [insert availability]. 

Regards,

[Your name] 

Notice that the above suggestion has two key requirements for it to be fulfilled: 

1. It assumes you’ve got an online presence; if you don’t, make sure you at least create a LinkedIn account and optimize your profile.
2. It assumes you’ve conducted vigorous research about the company’s machine learning department since you must be in the loop if you want to add value. 

But don’t stop there. Recruiters are extremely helpful for landing your first job, so it’s vital that you also try to connect with recruiters through platforms like LinkedIn. Build a relationship and let the recruiter know the type of work you’re interested in so they can be on the lookout for you. 

A major disclaimer is that this is not guaranteed to land you a job. However, the systematic approach to the job hunt allows you to track your progress better and improve in areas that you do not do so well. For example, if you reach out to someone and don’t receive a response, you can tweak that message and send it to someone else. If it gets a response, you can alter that message and use it for someone else.  Ideally, you would keep tweaking it until you receive more and more responses. 

What to Expect in a Machine Learning Engineer’s Interview

Different companies have their preferred way of conducting their interview process, and finding each approach can be challenging. A good practice is to ask how the interview process works before your first interview, but this information is usually given to you. In addition, most companies tend to take their approach from multinational organizations (i.e., Google, Facebook, Apple, etc.) and then add their twist to suit them. Thus, we can learn a lot about how most companies conduct machine learning engineer interviews and get a better idea of what to expect by looking at the processes of multinational organizations. 

Google interview 

Google seeks to hire only the brightest talent. Consequently, its extremely challenging interview process is designed to filter out candidates who do not meet its high standards. 

The interview process is also extremely broad and specific to Google (i.e., Google Cloud), covering various topics from data structures and algorithms to system design and testing. You can expect to go through several rounds, including a recruiter screen, one or two technical phone screens, and four to six onsite interviews.

Amazon interview

Like Google, Amazon’s interview process is specific to Amazon (i.e., AWS) and extremely difficult. The interviews include a recruiter phone screen, an online assessment in some instances, one or two phone screens, and four to six onsite interviews. 

The topics to be covered include behavioral questions, software engineering questions (i.e., system design), and machine learning-specific questions. However, it’s possible an interviewer may ask about some of your machine learning projects and require you to solve a coding problem.

Meta interview 

Meta’s machine learning engineer interview process is quite holistic. You’ll be taken through a recruiter screen, a coding interview, and about five onsite interviews to determine your suitability. You may also receive a take-home assignment for the hiring managers to see how you work through problems practically. 

It’s important to note that not all companies' hiring processes are as prolonged or intense as the multinational companies listed above. For example, some companies do not believe it’s as necessary to hone in on data structures & algorithms.

Still, most would agree that machine learning system design is essential and would include a section to test your knowledge in that department. Therefore, you should expect multiple interview rounds- typically a screening round, then a technical round, followed by a behavioral interview - before a decision is made.

Machine Learning Engineer Salary Potential

How much you can earn as a machine learning engineer depends on your location. For instance, the average base salary for a machine learning engineer in 2024 in the United States is $161,000, according to Indeed. 

It’s clear to see that this may need some refining since many companies are now accepting more and more remote workers. There has been an ongoing debate on how to pay employees fairly given the rise of remote work: some companies have resolved to pay employees based on their location, which means you could earn less than someone in the same role as you if you’re working from a lesser economically developed country and they’re in office.

Other companies have decided to stick with the pay rate regardless of the location. The main gist is that companies have different policies about paying remote workers, so you’ll have to do your due diligence. 

Check out our dedicated article on machine learning engineer salaries for a more in-depth analysis.

Conclusion

The outcome of a machine learning engineer's workflow is a data product. To work effectively as a machine learning engineer, you must be a technically sound programmer with a solid math, statistics, probability, and software engineering foundation. Although it’s often requested in job descriptions, a degree is generally not required for most companies, but it is necessary to demonstrate your capabilities with a portfolio.

DataCamp has excellent career tracks to get you started on your journey as a machine learning engineer: 

• Machine Learning Scientist with R
• Machine Learning Scientist with Python