What is a Machine Learning Engineer?

What is a Machine Learning Engineer?

It may surprise you to know just how many mobile apps and tools you use in your day-to-day are powered by machine learning (ML). Machine learning models are used in various real-world applications. From fraud protection to facial recognition, it's deeply integrated into the digital world we live in.

More broadly speaking, a machine learning engineer designs and implements various machine learning models and systems. They are responsible for training the machine learning models with specific data sources and developing Artificial Intelligence (AI)-powered algorithms for the different systems. They are also responsible for testing, validating, and deploying machine learning models into larger applications.

So, what exactly is a machine learning model? Machine learning models are created by engineers and used in real-world applications to provide product recommendations and automate simple tasks like email spam filtering. These models are integrated into everything we do and are vital to the customer experiences companies aim to provide.

The importance of machine learning engineers in 2024

In 2023, Indeed ranked machine learning engineers as one of the top 10 jobs of the year. With the rise of artificial intelligence and other machine learning technology, these roles are now more important than ever. Machine learning and AI jobs are expected to grow by 40% between the year 2023 - 2027. Now is a great time to fine-tune your skill set and hone in on your talents so you can land a fulfilling job as a machine learning engineer.

In this article, we'll explore the responsibilities of a ML engineer and why this job is important to the current tech landscape.

Here's everything you need to know about starting a career in machine learning:

Responsibilities of a machine learning engineer

A machine learning engineer works directly with a data science team to design systems used in real-world applications. They help with researching, building, and designing the AI-driven tools responsible for machine learning. They also maintain and improve existing artificial intelligence systems. They are responsible for collaborating with other team members to develop prototypes and construct models for machine learning systems.

Before we dive into the different responsibilities of a ML engineer, there are a few definitions we need to cover.

What is machine learning?
Machine learning is a type of artificial intelligence that uses data and algorithms to mimic human behaviours. It’s used to help complete complex tasks, solve problems, or automate mundane tasks in the workplace.

What are ML systems?
ML systems manage data that filters through machine learning algorithms. The algorithm trains the machine that manages an application or service. There are four different types of machine learning systems. This includes real-time, batch, stream process, and embedded/edge applications. All four of these ML systems are designed to handle a different type of algorithm.

What is an AI algorithm?
Algorithms are a set of rules that are used to train the ML system. This set of rules uses data to teach the ML system to learn patterns or make decisions.

Some of the everyday responsibilities of a machine learning engineer include:

Designing and developing ML systems
Machine learning engineers are responsible for building and developing artificial intelligence systems and algorithms.

Algorithm development
A machine learning engineer is responsible for developing the machine learning algorithms that power these different types of ML systems and making systematic updates.

Data analysis and data visualization
Data is vital to making meaningful changes to the ML systems and the algorithm that powers them. A machine learning engineer needs to know how to perform statistical analyses and to have an eye for visualizing data.

Testing, optimizing, monitoring
Once an ML system has been created, the machine learning engineer monitors the learning model and optimizes it when needed. They also test and optimize current machine learning algorithms and data processes they use to find deployment issues, treat missing values, or add more data if necessary.

Educational and professional background

Most companies hiring for ML engineers are looking for candidates with background knowledge or education in data science or machine learning. Entry-level roles you could get to start your career journey of becoming an ML engineer include:

• Software Engineer
• Software Programmer
• Data Scientist
• Computer Engineer

These roles will provide the hands-on experience needed to find a full-time career in machine learning in the future.

Three ways to become an ML engineer

Self-study
Picking up essential skills on your own time through self-study can be an excellent way to test if the job is for you and if you want to further your career as an ML engineer. Taking the self-study approach also shows employers that you take the initiative and are enthusiastic about the role.

• Pros: Self-study can be done on your schedule and is affordable. There are many free or inexpensive online courses.
• Cons: The lack of deadlines may be challenging for those who benefit from a more structured learning approach.

University
Many universities offer computer science and engineering programs or programs in a related field. You may also be able to pass directly to a post-graduate program, depending on your past experience. Master's specializations can take anywhere from 1 to 3 years.

• Pros: Looks good on a resume and can add legitimacy to your application.
• Cons: Longest option on this list and the most expensive. If you choose to pursue a bachelor's degree, you'll spend 4+ years changing careers.

Bootcamp
Bootcamps, like Lighthouse Labs Data Science Program, offer a fast-tracked way into the tech sphere. You'll gain the industry skills employers seek. Bootcamps can also come with career and mentoring support.

• Pros: Good cost-to-program length ratio. It is usually the best option for those changing careers as they enter the job market quickly.
• Cons: Bootcamps are intense and require a lot of focus and dedication to complete a large amount of material in a shorter time.

Key skills and tools for success

Companies looking for machine learning engineers are searching for specific skills and tools. Some of the key skills you should have on your resume include:

Experience with programming languages
The top companies hiring for these positions want to see that candidates have experience with essential programming languages like Python, Java, and C++. Here's how these three programming languages differ:

• Python: Python is the easiest programming language to learn. It's simple, easy to read, and versatile. Many machine learning engineers prefer this programming language because it provides high-level abstractions and is an interpreted language. Python is commonly used for machine learning and data science.

• Java: Java was launched in 1995 and is arguably one of the most popular programming languages. It's commonly used for games and desktop and mobile applications but can also be used to power complex machine learning algorithms. Many machine learning engineers prefer Java over other programming languages because it does not require special hardware or software to run.

• C++: C++ is an extension of C language, a popular language programming model used in the 1980s that many machine learning engineers use in their day-to-day. It offers a standard template library and incorporates object-oriented programming concepts. C++ is commonly used for web browsers, operating systems, and cloud systems. It’s also a reliable programming language for ML models and algorithms.

Technical skills
There are also many technical skills a machine learning engineer will need to know to find success. This includes data structures, algorithms, and statistical methods. Many individuals need a strong background in applied mathematics or physics or need a strong understanding of basic math concepts, strategies, and tools. The most common math concepts that will help you succeed in this role include linear algebra, statistics, and probability theory.

Other skills that recruiters and companies are looking for include:

• Data modeling and data visualization
• Natural language processing
• Data visualization

Career path and job market

The current career progression for a machine learning engineer shows that these jobs will play a crucial role in the tech industry. With the rising demand and the new artificial intelligence developments, we're sure to see this average salary continue to increase in the machine learning job market.

Job Title	Average Salary
Data Analyst	$67,703 /yr
Data Scientist	$98,957 /yr
Machine Learning Specialist	$92,019 /yr
Machine Learning Engineer	$111,763 /yr
Sr. Machine Learning Engineer	$150,564 /yr

The best way to beat the growing competition is to hone your skill set and find ways to differentiate yourself from the other candidates in the machine learning job market.

Real-world applications

The rise of artificial intelligence has increased the demand for machine learning engineers. There are many use cases for ML systems and machine learning algorithms. In fact, you likely use a few real-world applications in your everyday life. Some popular uses include:

Facial recognition
Facial recognition can be used to protect your personal information and prevent people from breaking into your phone. This provides an enhanced level of security that far outweighs the power of a simple four-digit passcode. Facial recognition is a popular ML algorithm that is trained to analyze facial features. These systems are designed to learn the patterns and unique facial characteristics of different people. The data processed in the algorithm helps identify and verify an individual based on facial data the algorithm is trained on. Facial recognition was popularized by the iPhone. But it is also used in surveillance, security, and government offices. This type of real-world application uses Python. It also has a similar algorithm to image recognition.

Spam filtering for emails
Scammers send spam emails in an attempt to install malware onto your computer or to get you to provide sensitive information. These emails may look like the real thing, but they are just phishing attempts that put you and your personal information at risk. The good news is that you can also train systems and algorithms to identify spam emails. These ML systems are built into email providers like Yahoo or Gmail. They are designed to protect individuals from scams or potentially dangerous emails. These algorithms are trained to identify spam emails through content analysis. The system looks for keywords and suspicious links similar to other spam emails the algorithm is trained on. These spam filtration models protect individuals from potential viruses and malware. They have become vital to everyday cybersecurity and continue to advance with the rise of artificial intelligence.

Personalized product recommendation feature
Personalization is vital to the customer experience. The personalized product recommendations you receive on Amazon or TV show recommendations on Netflix influence the customer experience. These recommendations improve engagement and retention, keeping you coming back for more. Sales and marketing teams use it to connect with customers and to promote their business. Personalized product recommendations use a recommender system. This ML system is trained on the shopping behaviors and habits of the individual. They then filter this information through the machine learning algorithm to create individualized recommendations.

Frequently asked questions about careers in machine learning

What do I need to be a machine learning engineer?
Most individuals wanting to start a career in machine learning need some experience in an entry-level position, like a software engineer or a data scientist.

How long does it take to become a machine learning engineer?
With the Lighthouse Labs Data Science Program, you could be ready to start your job search in as little as 12 weeks. Depending on your past experience, you may be able to enter directly into a machine learning role; otherwise, you will likely start in a data science role and then advance into machine learning positions.

Do machine learning engineers code a lot?
Most ML engineers need basic programming skills. Programming languages are vital to the foundation of machine learning systems. However, you may not find yourself coding as much as a full-stack developer or system engineer.

Are ML engineers in demand?
Yes, careers in machine learning are in demand. The demand for these jobs is expected to increase between the years 2023 - 2027.

Sign up for the Data Science Program today

Ready to find a career in the machine learning job market?

At Lighthouse Labs, we're here to help you gain the skills for your next role. Our Data Science Program is the quickest way to launch a career in machine learning. We offer an online 12-week intensive program or a 30-week Flexible option. The program includes on-demand mentorship and career services you can benefit from for LIFE. At the end of the program, you'll have the tools and skills needed to level up your resume and find a career in data science.