Artificial Intelligence

Should You Become a Machine Learning Engineer?

Should You Become a Machine Learning Engineer?
Artificial intelligence will create 2.3 million jobs in 2020. As a machine learning engineer, you'll be at the forefront of AI opportunities, and you'll maintain a prosperous job outlook well into the future Image from Unsplash
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Mary Kearl December 31, 2019

If you've been obsessing about artificial intelligence (AI) since "The Terminator," you're in luck: AI is everywhere, and it still needs live humans to make it happen. As a machine learning engineer, you'll code and analyze programs to help a company or institution make the most of its data. Perk: You're one step ahead of a robot taking your job—for now.

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Did you know that 80 percent of the content watched on Netflix results from the streaming service’s recommendation engine? The engine looks at what you watch, compares it to similar viewers’ preferences, and then recommends a list of choices it thinks you will enjoy. Four out of five times, Netflix customers select something from that list. It’s a prime example of machine learning engineering in action.

Machine learning engineer is a fairly new job title, but it’s already got some people’s attention. Indeed named it the “Best Job of 2019,” and it’s one of the top-growing positions in LinkedIn’s annual “Emerging Jobs Report” in 2017 and 2018. This is a career definitely worth considering; this article can get you started. This guide to becoming a machine learning engineer will cover:

  • What is a machine learning engineer?
  • Requirements for being a machine learning engineer
  • How to become a machine learning engineer
  • Skills needed to become a machine learning engineer
  • Should you become a machine learning engineer?
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What is a machine learning engineer?

Artificial intelligence (AI) is the umbrella topic under which machine learning and other sub-specialties fall. AI is how machines do their thinking; machine learning is the process by which they gather data and use what they’ve gathered to become “smarter.” Through machine learning, systems actually improve without additional human intervention. Machine learning engineers make it all possible.

What does a machine learning engineer do?

From beer brewers to toy manufacturers, nearly every type of modern business is trying to figure out how to use data effectively. Machine learning engineers help companies act quickly and evolve to meet or exceed their key performance indicator (KPI) targets.

Some of the main roles and responsibilities of a machine learning engineer include:

  • Producing a company’s adaptive learning algorithms and capabilities
  • Conducting analyses and executing changes based on the findings
  • Staying up-to-date on machine learning best practices that may impact the business

Like other software engineers, machine learning engineers must be able to write software code, so programming and software engineering skills are a must. This may involve creating machine learning algorithms, but more often requires integrating existing algorithms and models into software.

What it’s like being a machine learning engineer

Perhaps the best part of the job is the generous compensation: the average base pay for machine learning engineers is $121,292 per year, according to Glassdoor. Payscale reports an average annual salary of $111,657, noting opportunities for additional compensation (an average bonus of $10,075, average profit-sharing of $10,153).

Expertise in machine learning positions you to work at industry-leading, cutting-edge companies like:

  • Adobe
  • Spotify
  • Facebook
  • LinkedIn
  • Google
  • eBay
  • Nike

The gender disparity among machine learning engineers is reflective of the whole tech industry; about 12 percent are women. The numbers represent both an opportunity and a risk for women considering the profession. On the one hand, companies are anxious to diversify, which should create opportunities. On the other hand, a “boys’ club” atmosphere in some shops could lead to uncomfortable or downright hostile work environments for women looking to enter.

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“I'm Interested in A.I. & Machine Learning!”

The tech industry is known for welcoming those with significant hands-on experience. Plus, the demand for AI professionals is rapidly increasing. Many AI job postings you’ll spot today will only require an undergrad degree and a list of specific required skills. However, when you browse highly sought-after positions at well-known firms, the jobs often require a graduate degree (PhD or computer science master’s). If you’re looking for high-paying positions in the field, a master’s is crucial. (source)

The earning potential for these roles typically soars past the six-figure mark. The median salary of an AI engineer, for example, is $164,800. A University of San Diego salary chart lists other six-figure AI-related positions, including data scientists (who earn $127,000), research scientists ($111,000), and big data engineers ($131,000). (source)

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Requirements for being a machine learning engineer

The majority of machine learning engineering professionals earn a bachelor’s degree in computer science, artificial intelligence, or a related field. Most positions require a strong grasp of common programming languages, such as C++ and Java, as well as advanced math and statistics.

What is the highest level of education required to become a machine learning engineer?

The short answer is that experience matters most when you’re building a career in machine learning. Practical skills can be achieved through master’s or PhD programs, but completing certificate programs (from companies like IBM) or university-sponsored bootcamps (like this one from Columbia University) can also boost your profile.

If you already have a background in software engineering or another technology and you’re looking to branch out to machine learning, online education platforms offer copious content and flexible learning schedules. Stanford University, for example, offers machine learning coursework via the online education platform Coursera.

Some of the top programming languages for machine learning engineers are:

  • Python
  • R
  • Java
  • C++
  • C
  • JavaScript
  • Scala
  • Julia

Luke Bilbro, data ops engineering lead at Tamr, offers this advice for aspiring machine learning engineers in Builtin:

  • Put on your data analyst hat and wrangle inconvenient data
  • Create effective solutions
  • Engage in verbal communication, and ask for help when you need it

How to become a machine learning engineer

With a bachelor’s degree and the appropriate level of experience, you may be able to land an entry-level engineering position. By completing certifications and continuing to add to your knowledge base, you will be able to advance, but the top jobs still usually require a graduate degree. It is common for people to gain experience in related jobs—such as programming, software development, or AI—before becoming machine learning engineers.

If you plan to earn a master’s in computer science or a related field, expect to commit two years (full-time) or more (part-time) to the endeavor. Combined bachelor’s and master’s programs are another option—allowing students to earn both in just five years.

What is the typical career path of a machine learning engineer?

Entry-level engineering roles typically provide a stepping stone on the career path to becoming a more specialized machine learning engineer. Machine learning engineers can progress from entry-level to managerial roles to senior and director positions.

Other related career paths include:

  • Data scientist: Conducts data mining and data analysis to create insights and recommendations for overall business strategies
  • Software engineer: Creates and maintains software
  • Quantitative analyst: Uses math to assess financial risk
  • __ PhD candidate: Prepares you to become a professor or research specialist

Skills needed to become a machine learning engineer

In addition to general machine-learning techniques like programming, supervised learning, and reinforcement learning, engineers need system-specific knowledge, according to LinkedIn’s “Emerging Jobs” report. This includes:

  • Deep learning: Machine learning models based on the human brain, through which algorithms are designed to learn by processing large volumes of data over time.
  • Tensorflow: An open-source machine learning platform where users can build and train their own ML models, allowing for experiments to be set up and deployed quickly. It has been used by Airbnb, Google, Twitter, GE, and CocaCola.
  • Apache Spark: An analytics engine designed to enable rapid processing of large-scale volumes of data.
  • Natural language processing: A type of AI meant to help parse out the meaning and nuance from the words we say or type to inform decision-making. Smart devices like Google Home or Amazon Echo and virtual assistants like Siri are powered by NLP.
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Should you become a machine learning engineer?

By 2025, global data creation is expected to reach 175 zettabytes (yes, that’s an actual measurement). That means the average “connected” person will accrue over 4,900 digital data engagements per day. If you’re looking for a boom market to drive your job search, you should be looking at data-related fields such as machine learning engineering.

Global research and IT advisory firm Gartner predicts artificial intelligence will create 2.3 million jobs in 2020. It can (and has been) said: The robots are taking over. As a machine learning engineer, you’ll be at the forefront of AI opportunities, and you’ll maintain a prosperous job outlook well into the future. If you enjoy problem-solving, geek out over data, and consider yourself an effective communicator, a career as a machine learning engineer may be a great fit.

Questions or feedback? Email editor@noodle.com

About the Editor

Tom Meltzer spent over 20 years writing and teaching for The Princeton Review, where he was lead author of the company's popular guide to colleges, before joining Noodle.

To learn more about our editorial standards, you can click here.


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