When you stop to think about it, the future can be a little daunting. It’s filled with AI, automation, 3D printing, virtual reality, IoT, and other concepts that until now seemed like science fiction. But if you understand these ideas, it can also be a place filled with opportunities. For example, by understanding the basics of AI and big data, you could carve yourself out a career as a machine learning engineer. Not only could that land you a very healthy machine learning engineer salary, but it could also help you to shape that very future.

In this post, we’ll take a look at what a machine learning engineer does, why it’s a great job role, and how you can get started.

Why machine learning?

Machine learning (ML) allows companies to make use of huge data sets for applications that would previously never have been possible. ML algorithms can learn the habits and buying behaviors of customers, perform incredibly complex mathematics, and enable entirely new products.

Almost every industry is going to be greatly impacted by AI and machine learning in the near future, and in ways that you probably wouldn’t expect. Take video games for example, where machine learning has made real-time ray tracing possible, resulting in photorealistic lighting. Every industry stands to be utterly transformed by the marriage of data and logic.

Also read: Is your job safe? Jobs that AI will destroy in the next 10-20 years

It’s for this reason that data scientist has been called the “sexiest job of the 21^st century” by Harvard Business Review.

What is a machine learning engineering salary like? According to Prospects.ac.uk, the average machine learning engineer salary in the UK is £52,000, which can rise as high as £170,000 if you work for a company like Google or Facebook. That’s around $62,568 or $204,551.65 respectively.

A machine learning salary can rise as high as $204,551

What is machine learning?

First, it’s important to understand precisely what machine learning is, and what it is not.

Machine learning is closely related to AI, but these are still distinct concepts. Whereas artificial intelligence can describe any type of program or machine designed to exhibit intelligent behavior, machine learning specifically means using algorithms to look for patterns in data. This can potentially be used to train certain types of AI.

AI that controls enemies in computer games does not typically use machine learning. Rather, it uses a kind of flow-chart for decision making, in order to respond to your actions with pre-set strategies. This is what we call an Artificial Narrow Intelligence (ANI) because it can only do one thing.

Also read: ML Ki: extracting text from images using google’s machine learning sdk

This is in contrast to Artificial General Intelligence (AGI), which is an AI designed to be able to handle multiple different types of task and even perhaps pass the Turing test.

Computer vision on the other hand – the ability of a program to identify objects in a scene – is accomplished via machine learning. By looking at hundreds of thousands of pictures, you can “teach” an AI to recognize objects like cars or plants. If your phone’s camera has scene detection, then this will use machine learning. Likewise, ML is also used to teach virtual assistants voice recognition.

Machine learning can be used to identify health issues from x-rays and assist doctors in their diagnoses, or to more accurately predict weather. There is far more potential yet to be tapped.

What does a machine learning engineer do?

The job of a machine learning engineer is to teach AIs and software using data.

The job of a machine learning engineer is to teach AIs and software using data. They might:

• Write programs and develop algorithms to extract meaningful information from large data sets
• Run experiments and test different approaches
• Optimize programs to improve performance, speed, and scalability
• Handle data engineering to ensure clean data sets
• Suggest useful applications for machine learning

A machine learning engineer might therefore work for a company that already produces a product — whether that’s voice recognition, computer vision, or something more specialist. Alternatively, they might work for an agency that provides machine learning solutions to businesses that can benefit from the technology. Or perhaps they might work in the R&D department for a tech company like Google to create new applications.

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There is some overlap between the roles of a machine learning engineer and a data scientist. Likewise, you might be required to call upon skills such as data mining, predictive analytics, mathematics etc. However, the role of the ML engineer is more specific, applying that knowledge in a very particular manner.

And of course, the machine learning engineer salary tends to be greater to reflect this.

To get an idea of the kind of thing you’ll need to understand as a machine learning engineer, I recommend this post on the top 10 algorithms used in ML. If that’s fascinating to you, then you will probably enjoy ML. If not, you might be better suited to another role.

How to become a machine learning engineer

Interested in becoming a machine learning engineer? Think you have what it takes? Here’s what you need to know to get started, and to land a great machine learning engineer salary.

Also read: How to work as a software developer online: Everything you need to know

In terms of qualifications and certifications, there is no set path to becoming an ML engineer. A lot of the jobs paying the best machine learning salaries ask for an undergraduate degree. This will often be a computer science degree, which will provide a broad understanding of computers, technology, and programming. A degree in mathematics can likewise be a great starting point.

Ideally, you would then build on this with a background in software engineering and data science. The most useful programming languages in this field are Python, C, and C++.

From there, you can transition to more specialist roles in machine learning, or tailor your resume with the machine learning courses below. Experience with ML APIs such as TensorFlow and Keras will also be extremely useful.

Also read: How to use LinkedIn and land your dream job!

Due to the huge amount of processing power and storage necessary to handle the massive data sets associated with machine learning, you will largely be working with cloud-based systems. To that end, it is also important to demonstrate familiarity with distributed computing.

As machine learning engineering is such a cutting edge career, there is no one path to follow. You might even find that you can get a long way as a self-taught programmer if you’re able to build up a strong enough resume.

Courses and certifications

Here are some courses and certifications you can use to get ahead as a machine learning engineer:

Bachelor of Computer Science – This is a full online bachelor’s degree course from the University of London that will provide the perfect foundation for those that are able to dedicate the time. You’ll study for 3-6 years, and be required to put in 14-28 hours per week.

Data Science: Machine Learning – If you already have some background in programming and/or mathematics, then adding specific machine learning knowledge may be all you need. This is a free 8 week course from Harvard University. You can add a verified certificate for a small fee, and it will also count toward a Data Science Professional Certificate should you wish to pursue it further. You can find that full course here.

Foundations of Data Science: Computational Thinking with Python – Another free course, this time from Berkeley University of California. It is 5 weeks long, requiring a commitment of around 4-6 hours each week. You can pay a little extra to add a verified certificate, or you can count it toward a full professional certificate in Foundations of Data Science.

Machine Learning Specialization – This machine learning specialization from University of Washington is comprised of four separate courses and is free to enroll. You’ll receive a course certificate that you can add to your LinkedIn or CV.

Programming in C# – This exam from Microsoft counts as credit toward an MCSA, but will also help you to bulk up your CV with evidence of relevant coding skills all on its own!

Also read: Microsoft Certification: A guide for tech professionals

Learn Python Programming Masterclass – This course from Udemy won’t provide a professional certificate but is an affordable and helpful introduction to this in-demand programming language.

So there you have it! That’s what you need to know to become a machine learning engineer. Is this a career you’d be interested in pursuing? Are you already an ML engineer? Share your tips and experience in the comments down below!

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