More and more tech professionals want to become machine learning (ML) engineers as the field is growing pretty fast. Today, it is a lucrative and exciting career choice.
According to the World Economic Forum’s 2023 Future of Jobs Report, this field is expected to grow by 40% generating more than one million jobs.
ML is a subset of artificial intelligence (AI) that helps predict and adapt outcomes as more data is received. It is driven by the concept of data-driven decision-making and relies on specific programming rules and machine learning algorithms to analyze vast amounts of data.
We explain how you can become a machine learning (ML) in 2024. Also, have a glance at the life of an ML engineer, and the qualifications, and technical & soft skills you will need to land this role.
Who is a Machine Learning Engineer?
A skilled professional who designs, develops, and implements ML models and systems is known as a machine learning engineer. They use different models to make predictions or decisions based on data collected from different sources.
ML engineers are at the intersection of data science and software engineering. They are responsible for turning insights obtained from analyzing data into practical and scalable applications.
Creating algorithms that help computers learn and make data-based decisions is one of their prime responsibilities. To work in this role you have to be proficient in programming, data analysis, mathematical modeling, and other such concepts.
Also read: What is Machine Learning? A Comprehensive Guide
7 Steps to Becoming a Machine Learning Engineer?
Let’s look at the 7 steps you need to follow to become a machine learning engineer:
- Obtain relevant degrees: A bachelor’s degree in fields such as computer science, mathematics, statistics, and other related domains will provide you with a foundational knowledge of machine learning. A master’s degree in AI, data science, or related fields will expand your knowledge and understanding of different ML concepts and help improve your job prospects.
- Learn programming languages: As an ML engineer, you will need to learn programming languages like Python, Java, or R, etc. One of the most popular programming languages is Python because of its extensive libraries such as TensorFlow, PyTorch, etc.
- Study ML algorithms and concepts: A machine learning engineer should know about ML algorithms and concepts like supervised and unsupervised learning, reinforcement learning, neural networks, etc.
- Gain practical experience: You need to work on different real-world projects to gain knowledge and experience. Participating in internships, hackathons, and competitions can give you such experience.
- Learn data handling skills: An ML engineer must know how to handle vast amounts of data. They have to work with large datasets, data preprocessing, and data visualization. In this regard, they also must know SQL and big data technologies like Hadoop and Spark.
- Have a strong mathematical background: An ML engineer should have a strong background in mathematics so that they can easily understand different statistical concepts. An understanding of linear algebra, calculus, probability, and statistics can come in handy in this role.
- Stay current with the industry trends: The ML domain is continuously evolving and therefore to succeed in this sector, an ML engineer must stay updated with the latest trends and advancements. They must know all the latest and best practices.
Also read: Artificial Intelligence vs Machine Learning: 9 Key Differences
Life of a Machine Learning Engineer
The life of an ML engineer can be both exciting and challenging. They face several issues daily that push them and force them to think outside the box.
These professionals must possess the skills of both a data science professional and a software engineer because often they have to work in cross-functional teams that include data scientists, software developers, and others.
A machine learning engineer does coding, experimenting with different algorithms, debugging, and optimizing models. They also read different online resources to stay updated with the latest advancements in the machine learning domain.
They frequently attend conferences and study online to earn certifications and keep their skills up to date.
Qualifications and Skills for a Machine Learning Engineer
The machine learning industry is very competitive and demanding. They must be master of several skills and have relevant qualifications. Let’s look at the educational requirements and technical & soft skills you might need to become an ML engineer.
Qualifications
A bachelor’s degree in computer science, mathematics, or a related field is the most basic requirement to become an ML engineer. A master’s degree or PhD will help expand your knowledge base and make you reach more complex roles.
You can also opt to do certifications. Professional certifications in machine learning and related fields will help enhance the quality of your resume.
Also read: Machine Learning vs. Data Science — Which Has a Better Future?
Technical and Soft Skills
Proficiency in technical and soft skills will help you prepare better for the role and perform optimally. Let’s look at some of the top technical and soft skills for this role:
- Programming skills: You must be able to code in languages such as Python, R, Java, and others.
- Knowledge of mathematics: Strong knowledge of linear algebra, statistics, probability, calculus, etc. will help you analyze large data sets and perform complex calculations with ease.
- Machine learning frameworks: As an ML engineer you should know different machine learning frameworks and libraries like TensorFlow, PyTorch, and others.
- Problem-solving: As an ML engineer you will face several complex problems daily. Therefore, you should be able to think outside the box, come up with unique ideas and solve problems.
- Communication: This is an important skill for an ML engineer. As an ML engineer, you will interact with several sets of individuals with different backgrounds. You will have to explain your findings, observations, suggestions, and technical concepts, sometimes even with non-technical stakeholders.
Machine Learning Engineer: Salary in the US
Some of the highest-earning individuals in the tech world are ML engineers. Their minimum salary in the US is $82,000 and the highest is $161,000, while the average annual salary is $118,500.
Now let’s look at the average annual salary of an ML engineer in the top companies in the US:
Source: Indeed
The below table shows the average annual salary of an ML engineer in different cities in the US:
Source: Indeed
FAQs: How to Become a Machine Learning (ML) Engineer
What tools and software do machine learning engineers commonly use?
Machine learning engineers frequently use tools and software such as TensorFlow, PyTorch, scikit-learn, Jupyter Notebooks, and data visualization tools like Matplotlib and Seaborn. They also use integrated development environments (IDEs) like PyCharm and VSCode.
What is the difference between a data scientist and a machine learning engineer?
While both roles work with data, a data scientist focuses more on extracting insights from data and building statistical models. In contrast, a machine learning engineer is responsible for implementing those models into production systems, ensuring they work efficiently and scale effectively.
How important is domain knowledge in becoming a machine learning engineer?
Domain knowledge can be very important as it helps machine learning engineers understand the context and specifics of the problems they are solving. This knowledge enables them to design better models and choose the right algorithms that are well-suited for particular applications.
Can a machine learning engineer work remotely?
Yes, many companies offer remote work opportunities for machine learning engineers. However, this can depend on the company and the specific job requirements, as some projects may require close collaboration with on-site teams or access to specialized hardware.
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