Machine learning engineers design, build, and deploy machine learning models to solve real-world problems. Explore a day in the life of an ML engineer.
What Does a Machine Learning Engineer Do?
Eerie dystopian predictions of self-aware robots taking over the world are currently stimulating the imaginations of many. Have you ever wondered about the truth behind these urban legends? Machine learning engineers have the answers.
If you are looking for an in-demand career on the cutting edge of technology, machine learning might be the field for you. Machine learning (ML) engineers design, build, and deploy machine learning models in order to solve real-world problems. But what does that mean for you and your career prospects?
Below, we will outline some of the day-to-day tasks ML engineers encounter. We’ll also talk about their work environment, work/life balance, and the skills and education you will need to become an ML engineer.
Responsibilities of a Machine Learning Engineer
The exact responsibilities of a machine learning engineer will differ depending on the industry they work in and the specific goals of the company that employs them. Workloads will also differ depending on whether you are part of a team, lead a team, or work as the company’s sole ML engineer.
Your tasks may include:
- Identifying problems—Communicating with stakeholders regarding needs and goals, and then identifying problems within that scope that could be solved by machine learning.
- Collecting and cleaning data—Obtaining data relevant to the problem, sorting, verifying, and securing it. This data will be used to train the machine learning model.
- Choosing algorithms—Selecting an algorithm relevant to the problem at hand. This requires an in-depth knowledge of available machine learning algorithms, including their strengths and weaknesses.
- Training models—Feeding the cleaned data into the algorithm so that it can learn.
- Evaluating—Using a “held-out” data set new to the freshly trained model, evaluating its performance. You will need to look for biases and incorrect outcomes.
- Deploying—Making the model available to users, whether within the business itself or as part of a customer-facing product.
- Monitoring and maintaining—Adjustments will almost certainly be needed, so you must regularly monitor the performance of the model and retrain it using new data when appropriate.
Also read: Remote Engineer | 7 Tips on how to Work Effectively as a Remote Engineer
What Is Working as an ML Engineer Really Like?
What does the workplace and work/life balance of an ML engineer look like?
Most ML engineers work full-time in an office, but working remotely or maintaining a hybrid work schedule is becoming increasingly common. Many machine learning tasks can be performed from a home office or laptop.
Machine learning engineers can expect a hefty salary of more than $136,000 per year, on average—more than $65 per hour. The luxuries such a salary affords can help balance the potentially long hours or overtime you may be required to put in.
Is it difficult to get a job as a machine learning engineer? Candidates possessing this skill set are in high demand across industries, as businesses seek to leverage machine learning to improve decision-making and other operations. According to the U.S. Bureau of Labor Statistics, computer and information research scientist jobs, which include machine learning engineers, are growing at a rate of 23 percent, which is much faster than average.
Wildly different industries are driving this increase. You might find yourself working in:
- transportation – engineering AI software for self-driving cars;
- healthcare – training algorithms to detect or diagnose disease;
- finance – creating tools to detect fraud or predict stock market trends;
- manufacturing – engineering robots to alleviate supply chain issues;
- advertising – using AI to predict a campaign’s potential effect.
Of course, you will need to meet the requirements in order to become an ML engineer. We’ll discuss these in the next section.
Also read: Overfitting And Underfitting in Machine Learning : A complete guide
How to Become a Machine Learning Engineer
Machine learning engineering is not an entry-level job. Typically, you will need a master’s degree in computer science or a related field for most job offerings. Federal government jobs in machine learning, on the other hand, may require only a bachelor’s degree.
If you’re just getting started, consider a computer science degree path. You should become familiar with operating systems, data structures, algorithms, and programming languages such as Python and R. Additionally, pay special attention to soft skills such as problem-solving, analytical skills, attention to detail, communication, and teamwork.
Then, start looking for jobs or internships that utilize your skill set. These may include junior roles in machine learning, business intelligence, data analysis, or AI engineering.
Network with others in the field—you never know when you may hear about an opening or even receive a recommendation from one of your professional contacts.
Different employers may require different certifications. Common certifications include ITIL®** Foundation Certification and CompTIA Project+. You can also increase your experience by working on personal or open source machine learning projects.
Key Takeaways
Are you ready to start your machine learning career? Begin by expanding your knowledge, earning a degree, and seeking certifications. With hard work, perseverance, and networking, you can obtain the coveted role of machine learning engineer and future-proof your career.
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