Anyone with “machine learning” in their job title, or even in their sphere of knowledge, is in a good career place these days. People with skills and experience in machine learning are in high demand, and that definitely includes machine learning engineers.
According to the research firm Markets and Markets, the demand for machine learning tools and systems is expected to grow from $1.03 billion in 2016 to $8.81 billion this year, at a compound annual growth rate of 44 percent.
Organisations worldwide are adopting machine learning to enhance customer experience and gain a competitive edge in business operations.
The growth of data is contributing to the drive for more machine learning solutions and skills. Examples of applications in key verticals include fraud, risk management, customer segmentation, and investment prediction in financial services; image analytics, drug discovery and manufacturing, and personalised treatment in healthcare; inventory planning and cross-channel marketing in retail; predictive maintenance and demand forecasting in manufacturing; and power usage analytics and smart grid management in energy and utilities.
In machine learning, individuals design and develop artificial intelligence (AI) algorithms that are capable of learning and making predictions. Machine learning engineers are typically part of a data science team and work closely with data scientists, data analysts, data architects, and others outside of their teams.
Machine learning engineers are advanced programmers who develop machines that can learn and apply knowledge independently. Sophisticated machine learning programs can act without being directed to perform a given task.
Machine learning engineers need to be skilled in areas such as maths, computer programming, data analytics and data mining. They should be knowledgeable about cloud services and applications. They also must be good communicators and collaborators.
The professional social networking site LinkedIn, as part of its 2022 LinkedIn Jobs on the Rise research, listed “machine learning engineer” as the fourth fastest-growing job title in the United States over the past five years.
There are some key qualifications you’ll need to become a Machine Learning Engineer. Overall, this role is responsible for designing machine learning applications and systems, which involves assessing and organising data, executing tests and experiments, and generally monitoring and optimizing the learning process to help develop strong performing machine learning systems.
As a Machine Learning Engineer, you’ll work to apply algorithms to different codebases so experience in software development is perfect for this position.
The perfect blend of math, statistics, and web development will give you the background you need – once you have a grasp of these concepts, you’ll be equipped to apply to Machine learning Engineering jobs.
If you don’t have that experience, you can still work toward a career in machine learning. First, you’ll need to first understand basic machine learning methods and the tools required to implement, use, and optimise machine learning algorithms.
Many people opt to complete a data science bootcamp or machine learning course to fast-track learning these fundamentals and work toward a job as a Machine Learning Engineer.
How to become a Machine Learning Engineer in six steps.
What Jobs Can I Get in Machine Learning?
People who specialise in machine learning can have a number of different titles and jobs, including:
Machine Learning Engineers run various machine learning experiments using programming languages such as Python, Scala, and Java with the appropriate machine learning libraries. Some of the major skills required for this are programming, probability and statistics, data modelling, machine learning algorithms, and system design.
Data Scientists analyse data to produce actionable insights, which are then used to make business decisions by the company executives. They use advanced analytics technologies, including machine learning and predictive modelling to collect, analyse, and interpret large amounts of data. A lot of people confuse Data Scientists and Machine Learning Engineers. To put it simply: a Data Scientist creates the required outputs for humans while a Machine Learning Engineer creates them for machines.
NLP Scientists (or Natural Language Processing Scientists) give machines the ability to understand human language. This means that machines can eventually talk with humans in our own language. An NLP Scientist essentially helps in the creation of a machine that can learn patterns of speech and also translate spoken words into other languages. Thus, a good NLP Scientist will be fluent in the syntax, spelling, and grammar of at least one language in addition to machine learning so that a machine can acquire the same skills.
Business Intelligence Developers use data analytics and machine learning to collect, analyse and interpret large amounts of data and produce actionable insights that can be used to make business decisions by the company executives. (In simpler words, using data to make better business decisions). To do this efficiently, a Business Intelligence Developer requires knowledge of both relational and multidimensional databases along with programming languages such as Scala, SQL, Python, and Perl. Some experience with business analytics services such as Power BI would also be an asset.
To get a machine learning engineer job, you’ll need to learn how to collect data, how different algorithms process data, how to diagnose results, and how to demonstrate business value to the organizations. These elements come with time, taking courses, and work experience.
A background in computer science, computer programming, software engineering, robotics, or deep learning will also help you land a coveted Machine Learning Engineer role.
Aside from education in one of these fields, there are multiple training programs you can take to help you build a niche expertise in machine learning specifically. These certificate courses will help take those proficient with math, development, or science and push them in the direction of a career in machine learning.
A high-quality machine learning course will teach you the foundational skills so that you have a comprehensive understanding of how machine learning and artificial intelligence works and how you can bring that technical perspective to the workplace. You’ll also be taught how to apply machine learning to real business problems and use real data to help leverage the decisions to these problems.
Finally, you can team up with our specialist consultants, who are working with the cutting-edge machine learning employers across the UK and have the industry know how to guide you in the right direction with impartial and trusted advice.
You can link up with our team by following this link and arranging a no-obligation conversation about your current situation and where you want to go or alternatively search our job board for the latest roles and apply directly with our consultants.
We hope you enjoyed our long read, the first of our career roadmap series. We will be looking at other exciting tech careers and the best route forward over the coming months so make sure you follow us on social media, so you never miss an update.