What Machine Learning and AI Engineers Do
Machine learning and AI are rapidly evolving fields, ideal for those who enjoy tech, but also have analytical minds suited to mathematics and statistics. On a broad scope, artificial intelligence (AI) is the concept that machines can do more beyond repetitive tasks; they adapt to various situations. Machine learning is a subset of this. It’s the concept that machines can not only be programmed to adapt, but to learn from their environments.
This is the tech that enables Alexa and Siri to answer your questions, and is also responsible for serving up ads and providing search results that are relevant to you when you surf the net. The field is popular with those who have an interest in software and application development/ engineering, as it requires similar programming skills and knowledge of various programming language. However, it differs in that machine learning and AI engineers create mathematical algorithms, which tell the system what kind of data to look for and what it should do with the data.
It's arguably one of the hottest careers to get into at present. Candidates emerging from top universities with machine learning and AI degrees at a graduate or doctoral level are scouted by large corporations and sometimes offered salaries which top a quarter of a million annually.
Who would enjoy a career in Machine Learning and AI Engineering?
The career is somewhat of a hybrid between software engineering, data science, and mathematics, so important to have a strong background in probability, statistics, and data modeling. Skilled developers must be able to look at large sets of data, uncover patterns, and make reliable predictions. Naturally, those with analytical mindsets and a love of tech do best. That said, there are many ethical concerns emerging on how machine learning can and should be used, so those entering into the field should be prepared for this. With the rapid progress being made in the field, people entering into a machine learning and AI career should also be prepared to continue learning as the tech evolves.
It is one of the more prestigious tech careers, so it’s well-suited to those who want to stand out and earn a larger paycheck. Lastly, many employers lack comprehension of what machine learning and AI development entails, which means many have unrealistic expectations for their engineers or don’t provide the resources necessary for engineers to be effective. Those with diplomacy and the ability to educate their peers, and often superiors, will find more satisfaction in their jobs.
Who mightn't like the career?
Many software and web developers/ engineers are feeling pushed into AI or are running to it to improve their job prospects. However, the data science and mathematics components differentiate the fields and it may not be an ideal move for people who don’t possess strong skills in those areas.
The career can also be quite stressful, as the push to develop flawless AI increases. The lack of support and resources from employers also keeps many developers on the move, constantly searching for a new job, so it may not be a good field for someone who isn’t prepared to do a fair amount of educating or who hopes to find success immediately with a single employer. It’s worth noting that the field offers top salaries, easily topping six figures, even for newcomers. However, breaking into the field can be a challenge for those coming in without a formal education focused on data science, AI, or machine learning, and there are large gaps of hundreds of thousands of dollars annually between those with expertise and a PhD behind them and those coming in from other IT disciplines. Hopeful candidates may find themselves disillusioned if their background is not in line with what large corporations demand.
Lastly, this is largely a male-dominated field, with as few as 13 percent of positions being held by women. Therefore, this may not be the ideal field for a woman who isn’t prepared to carve her own path.
As with many tech fields, those getting into machine learning and AI development are often self-taught or have taken upskilling classes related to the field to enhance their knowledge, though these individuals often find themselves working their way through several jobs before they find a good fit. However, more often than not, the career attracts people who are already in engineering or data science careers, meaning they typically have a master’s degree or PhD in a computer-related or mathematics-related field.
Knowing one or more of the common programming languages is essential. Presently, Python is the most popular for AI, though R, Java, C++, and others may be necessary. Familiarity with various tools and frameworks is also required.
Interviewees are often grilled regarding their data science and engineering knowledge. They may be tested on their ability to perform complex equations, solve problems, or identify patterns as well.
- Machine Learning Engineer Interview Questions
- 41 Essential Machine Learning Interview Questions (with answers)
- Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent
Moving into Machine Learning and AI Engineering from another career
Arguably, the easiest and most common shift is from data science to machine learning and AI engineering. However, software engineers and developers who are skilled with modeling, statistics, and probability also frequently make the shift too. Sometimes, this is done in-house, as the engineer or data scientist requests to be put on projects that offer exposure to machine learning and AI, though upskilling via online classes is a common path as well. Following this, many people in other engineering careers or tech careers may make the switch, provided they take the necessary classes. For further reading, see the Reddit Discussion “Where are you with your career in ML? Alternatively, how many are you are developers and now getting into ML?” and the article “How To Reinvent Your Career By Learning Artificial Intelligence?”
There isn’t a linear career path for machine learning professionals and the duties carried out will vary greatly based upon the position. In many cases, the engineer will carry out the duties of a data scientist in tandem with his or her own work, and may spend a great deal of time trying to clean up data so it can be used. Following this, emphasis is placed on creating algorithms and programming.
Machine learning engineers are not usually required to travel for work, but those seeking more lucrative positions may need to relocate to large tech hubs for employment.
According to data from PayScale, machine learning engineers have average salaries of USD $110,000 in the United States, £49,000 in the UK, and CAD $82,000 in Canada, while Indeed places Australian salaries at AUD $106,000.
Bonuses and profit sharing can be modest sums of just a few thousand dollars or they can nearly double a salary, depending on the position.
Why a Machine Learning and AI Engineer moves on
Most machine learning engineers love the profession as well as the pay and don’t leave the field altogether, but there are high rates of job dissatisfaction. Common complaints include being expected to clean the data, being expected to do the work of a data scientist in addition to their own duties, and underutilization of their skills. Because of this, there is quite a bit of shifting from job to job in the profession. However, those who do decide to leave altogether have a wealth of opportunity. For further reading, see “How machine learning creates new professions — and problems.”