How to become a machine learning engineer in 2023 is an essential question since the field has shown considerable growth in the last few decades and is a hot area in tech alongside artificial intelligence. Machine learning concepts help artificial intelligence models to excel in emulation by training machines to study and apply data sets efficiently.
The more tasks a machine learns to perform, the faster it can stop depending on human input for those tasks. This leads to improved business operations in general but is also impactful on day-to-day activities.
Machine learning is not a one-fits-all solution in any sense. It comprises several different skills, which is why it is essential to approach the topic with caution and the correct information. You do not need expertise in every skill related to machine learning, but the field can be challenging to break into compared to other tech fields, like web development.
In the following sections, we will explore what you can expect in a career with machine learning and the things you need to focus on to get your first job!
How to become a machine learning engineer in 2023?
If you want to become a machine learning engineer in 2023, it is essential to get very comfortable with Python and data science, as these two are critical when working with machine learning algorithms. It would be best if you tried to enrol in a course that incorporates actual projects where you can hone the skills in demand with modern businesses.
It is also essential to be proactive with job applications and reach out to different companies looking for the niche skill set your portfolio is based around.
Networking can help you get in touch with different hiring managers, and it is essential to remember that a strong network is potentially the best job security you can have
In the following sections, we will take a more in-depth look into the different elements that you need to consider to have the best odds at securing your dream job as a machine learning engineer:
Core programming skills
Programming is a big part of machine learning, and it is essential to remember that machine learning extends into the larger computer science field. If you want to land a job in the industry, you should showcase that you understand programming, data gathering and mathematical statistics. The best way is to get a bachelor’s degree or technical certification from a reputable course provider.
Hiring managers will expect to see an aptitude for problem-solving that incorporates programming languages like Octave, C++, Python and R. In general, understanding software design and development help develop good machine learning skills since you will optimise different computer software and applications to ensure optimal performance as part of your job duties.
Mathematics promotes logical thinking and mental rigour and helps develop mental discipline in any tech niche. However, when it comes to machine learning, the candidate needs to comprehend and use algorithms in various applications. Therefore, employers will search for an individual with excellent mathematics proficiency.
This is because mathematics is deeply incorporated into every stage of machine learning, regardless of the expertise level. Machine learning demands expertise in various mathematical ideas, from picking the most reasonable method of execution to selecting the appropriate parameter.
Linear algebra, calculus, statistics and probability theory are the four fundamental concepts that form the very basis of machine learning. Thus it is crucial to master each of these aspects thoroughly. However, the most vital mathematical concept in machine learning is linear algebra, which is a core tool for data transformation, dimensionality reduction, and model assessment.
The two programming languages every good machine learning engineer masters are Python and C++ and their associated libraries.
Python offers readable and concise codes. Since machine learning and artificial intelligence involve complex algorithms, the simplicity of Python programming adds value and enables the creation of reliable systems. If you want to learn more about how Python is used in data science, check out our detailed guide on the topic here!
NumPy is regarded as being one of the most widely used and best Python libraries for Machine Learning. Scikit-learn is a Python library that is used for classical machine learning algorithms. It is built on top of two basic Python libraries, NumPy and SciPy. Scikit-learn is popular among Machine learning developers as it supports supervised and unsupervised learning algorithms.
The algorithms that are part of data science don’t essentially need to originate from a strictly mechanical process. Therefore, it must be understood that machine learning is a branch of the larger data science field, not the other way around. Then, even within machine learning, several sub-systems and techniques are involved, supervised clustering and regression being two important ones.
Since artificial intelligence is excellent at simultaneously digesting lots of data, you must create AI software that makes data-intensive tasks quick and simple to utilise this technology efficiently. Examples of such tasks would be marketing or customer service system projects that require constant data updates and might be a hassle to operate accurately with front-end AI alone. To learn more about the differences between software and data engineering, you can check out our detailed guide on the topic!
If you are starting out in this niche, you may find it much simpler to spin up machine learning algorithms on a public cloud system. On the other hand, constructing your personal machine learning rig may be more sensible if you want to cut down on costs in the long term.
Projects and Algorithms
When it comes to machine learning, employers will be looking for candidates who know how to work their way through projects and algorithms of different types. Therefore, once you have completed your computer science degree, it would be ideal to start putting what you have learned into practice and develop your skills with artificial intelligence to make yourself stand out to future employers in the industry.
To get started, you can work with several machine learning resources that are available online, including various libraries, APIs and packages. These include H2O, Sci-Kit Learn, Keras, Spark MLib and more if you want to familiarise yourself with the standard techniques involved in machine learning.
Of course, it is critical to remember that you need to know the exact fundamentals and parameters before you work with machine learning techniques. This means ensuring that you picked the correct model to work with and the soundest strategy to apply.
After you fully understand how machine learning functions in real-world situations, it’d be an excellent idea to try developing your projects. This will be a learning experience with mistakes here and there, but the best part is that you will gain a lot of knowledge along the way and have the chance to put something valuable in your CV.
Resume and outreach
Lastly, it would be best if you focused on building up your network with good outreach skills, and it all starts with the portfolio and resume you assemble. These two factors help you stand out to hiring managers interested in seeing what problems you have solved in the projects you worked with.
If you have a portfolio developed a few years back, studying and redeveloping it to showcase skills in demand with modern companies and new technological innovations is essential.
Designing your portfolio and resume to showcase your progress and ability to learn new concepts and upskill is paramount. However, it is also important to carefully consider how easy it is to fall into the dangerous rabbit hole of assuming you know everything. This is where communities come in. They are places where you can keep track of all the updates and innovations within your industry.
Good communities will inform you of every new and relevant skill, and you can use them for networking with like-minded professionals. If you are starting as a complete beginner, this is an excellent technique to build up on your industry jargon and speak the “common” tongue!
What can you expect in a career as a machine learning engineer?
Machine learning is an integral part of AI and data science. Professionals in this field ensure that machines can learn to take different actions that reflect human involvement without needing direct guidance. This is achieved using multiple algorithms and tools, and some excellent examples include image recognition, product recommendations and self-driving cars.
Professionals must show they are comfortable with different technical tools and can contribute reasonable solutions that make it easier to fulfil business goals. This is much easier if you are naturally inclined towards problem-solving and implementing automation. If you like automating, analysing and gathering data, you can enjoy several job opportunities as a machine learning engineer with outstanding salaries.
Since machine learning is quickly growing to become one of the most prominent tech fields, companies offer excellent pay packages for skilled professionals. Of course, you can have experience running multiple decades, but it always helps to be excellent at what you offer. While the final amount will depend on different factors, like background, location and work experience, machine learning engineers can enjoy a pay that is at par with popular tech professions, often making more than $100,000 annually!
The data-driven industry relies on machine learning specialists, who know their way around advanced machine learning models, contributing to the skyrocketing demand for skilled professionals. If you want to pursue a career as a machine learning engineer in 2023, book a career consultation with one of our experts here, and get the right plan to follow!