What Does A Typical Machine Learning Career Path Look Like And How Can You Get Started?

So before you fall in love with the idea of a career in ML, it’s worth separating the fact from the (science) fiction.

In this article, we’ll look at what machine learning engineers actually do (spoiler: they aren’t all building robots!), what a machine learning career path looks like, what the pros and cons of the field are, and how you can start your journey.

What a career in machine learning really looks like

While you can expect a little bit of variation between postings — machine learning is fairly up and coming, after all — there are a few things you can expect if you pursue a career in this emerging field.

From an employer’s perspective, your role will almost always be to come up with ways to process massive amounts of data with machine learning strategies. For example, a machine learning engineer working for YouTube might look at the data being gathered from users, and turn this into a way to better optimize ads on the platform.

As you might imagine, this is complex work that often lacks a typical schedule. You may find yourself spending weeks on one aspect of your job, testing models and tweaking them slightly until you get your desired results. Other days, you’ll be working with data scientists on the theories supporting your work, so that they can better translate your findings.

In other words, you can think of a machine learning career as a science rather than a typical programming job with an AI twist. And no two days will ever be exactly the same.

What a machine learning career doesn’t look like

To the untrained eye, the concepts driving ML and AI can easily intermix. So let’s break it down…

• Machine Learning - building computer programs that can use their own experience to modify their approach, typically used to work through vast quantities of data or solve complex problems.
• Artificial Intelligence - building computer programs that mimic human intelligence, such as voice assistants and chatbots. Machine learning is one component of AI.

With these definitions under our belt, we can quickly see that a career in machine learning probably won’t mean building a sentient computer.

If your goal is to make the next Siri or Google Assistant, then you’ll want to pursue a career in AI, not ML.

If you’re keen to design self driving cars, though, then keep on reading…

The pros and cons of a machine learning career

Now that you have a somewhat clearer idea of what a machine learning career entails, it’s time to think about whether or not it’s the right sort of role for you. Below we’ve gathered some of the common pros and cons of the career, to help you decide:

The pros

The work is groundbreaking

In terms of sector maturity, ML is still in its adolescence — with the most exciting breakthroughs looming on the horizon.

Working in this field means you’ll be at the forefront of a technology that will dramatically affect the world as more time goes on. Your research, developments, and implementations will push your employer’s company forward — and the field as a whole! For this reason, it can be an extremely rewarding and exciting sector to work in.

It pays well

Like most software development jobs, machine learning engineers can expect a comfortable annual salary at worst and an excellent salary at best, depending on which companies you are working for. You can expect to make between $85,000 and $200,000 depending on your experience and employer.

The cons

It’s demanding

Like any groundbreaking field, machine learning is often exhausting and overwhelming. It requires copious amounts of research and a constant state of learning. There will always be new concepts to learn, and you’ll be continuously upskilling yourself in your mission to solve ever more complex problems.

When working with startups, you may feel as though you’re reinventing the machine learning wheel. While businesses that are further along may demand additional education, just to understand how their systems work!

Long story short: if you don’t have genuine interest in the field, you could end up burning out within a few years.

The work can be vague and unfocused

Remember how we said that no one day in ML will be like the last? Well there’s two sides to that coin.

For some, this might add to the excitement of the role. While for others, the lack of structure and focus might make the work feel stressful and unclear. If you’re someone that needs a routine to follow, a more typical programming role might be a better fit for you.

The million dollar question: is there a “typical career path” in machine learning?

For most careers, not only is there a standard career path that you can base your journey off of, but there are actual courses and programs designed to accelerate your development.

In machine learning? Not so much.

Instead, you’ll need to be very self-directed, and set your own milestones and skills as you progress. Here’s some food for thought:

• Getting started: Before you even apply for an entry level ML role, you’ll need basic programming skills, and a proven understanding of probability, statistics and data modeling. You can put this knowledge to work in your first job.
• Learning as you go: Building on your foundation, you should try your hand at more intermediate-advanced machine learning algorithms, while developing a working knowledge of software engineering.
• Honing your skills: What are particularly good at? After a few years in industry, now’s the time to start working towards ‘master’ status in certain skills and disciplines. Your job may create these opportunities for you, or you may have to set the challenge for yourself.

With so much to learn, becoming a ML engineer won’t be easy.

But is machine learning a good career choice? Yes! As long as you’re willing to put in the work. And that work can start today, if you want it to.

At MentorCruise, we’ve got a roster of talented, experienced and inspiring tech mentors ready to help you take your career to the next level. Fresh out of college and looking for your first gig? Great. Looking for a mid-career switch? Fantastic.

No matter where you’re at in your journey, or where you’re looking to go, we’ve got someone who can help.