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There’s an element of Frankenstein at play in the daily work of machine learning (ML) engineers. After all, these highly trained tech pros are, in a sense, bringing inanimate things to life — or at least endowing them with something like intelligence. How so?

Machine learning engineers create self-running software, which they pair with predictive models. After the predictive model carries out a given operation, the results are then automatically fed back into it by the software, which (ideally) leads to a cycle of perpetual improvement.

What are the top on-the-job priorities for machine learning engineers today?

  • developing customized software and predictive models to deliver on business goals
  • contributing to the design and development of machine learning (and/or deep learning) systems
  • testing and experimenting with new machine learning applications
  • researching, designing and implementing machine learning algorithms

In order to deliver on those priorities, the following specific activities often take up a lot of the workday for machine learning engineers:

  • designing machine learning systems that align with specific project objectives or business goals
  • collaborating with data scientists, natural language processing (NLP) experts and other key stakeholders
  • working with big data, data structures, data models and more in production environments
  • supporting software development efforts
  • researching and developing machine learning algorithms and related tools
  • building machine learning applications based on stakeholder requirements
  • identifying the appropriate datasets to use in machine learning tests and experiments
  • consulting on the best data-representation methods for machine learning projects
  • collaborating with cross-functional teams to identify where and how machine learning applications can contribute the most business value

how do you become a machine learning engineer?

There isn’t any one path to becoming a machine learning engineer — after all, jobs in this field barely existed a decade ago. Plenty of people who built careers in ancillary fields like data science, artificial intelligence (AI), natural language processing (NLP) and similar have successfully made the switch to careers as a machine learning engineer. So if that sounds anything like your background, you’re already more than halfway there.

The core requirements for landing a job as a machine learning engineer generally include the following:

  • master's or doctoral degree in computer science, math, engineering, physics or a related field (not always required)
  • anywhere from three to five years of previous professional experience in the field
  • proven ability to work with structured and unstructured data
  • experience validating statistical hypotheses and analyzing the performance of machine learning models
  • knowledge of how to support business use cases

what’s the difference between machine learning and data science?

Good question! A lot of people aren’t totally clear on this one, as a matter of fact. Here’s what you need to know:

  • Basically, data science should be understood as a fairly broad term: It’s a technique as much as an approach, and it comprises several disciplines — data analytics, natural language processing (NLP) and more. Machine learning is one of these disciplines.
  • At the same time, machine learning is very much alive and well as a vibrant field of its own. For example, machine learning engineers have developed specialized techniques. And what’s more, sometimes the very data being used and manipulated by data scientists is itself derived from machine learning processes.

Confused? We hope not. The key thing to remember is that the difference between the two terms primarily has to do with specificity — and that “data science” is the far broader term.

what are the key skills of a machine learning engineer?

Machine learning engineers are pretty busy people, as we have seen. To be effective in the role, they have to deal with wide-ranging stakeholders, understand business use cases — and while doing all of that, also stay up to date on the latest developments in their fast-moving field. Where are they going to find the time to search for their next great opportunity?

Let’s make that a little bit easier. Here are some of the skills and aptitudes you should be sure to showcase on your resume when applying for roles in this highly competitive space:

  • computer programming expertise
  • proficiency with multiple computer programming languages
  • native math aptitude
  • familiarity with cloud applications
  • strong written and verbal communication skills
  • analytical thinking
  • lifelong learning

Think you can convince a prospective employer that you've got all of these traits? The job should be all yours, if so. And if you think you need to brush up on your training or certification, simply check out the latest courses in machine learning from Udemy, our learning partner.

what is the salary of a machine learning engineer?

Compensation can vary considerably, so the following is based on a combination of sources. Actual compensation will likely depend on your location, market, responsibilities, background and relative level of expertise.







However, if you want to dive deeper into this kind of salary intel, you should check out our salary comparison tool. It’s free, easy to use — and it’ll help you discover data about compensation for machine learning roles across markets.

key takeaways

Think you’ve got it all down? Good! To quickly recap, in this article we’ve covered

  • what machine learning engineers do on a day-to-day basis
  • training, experience and other requirements for this position
  • key differences between machine learning and data science
  • skills needed to succeed in the role
  • salary expectations
  • plus, a whole lot more!

If you’re ready to take action at this point, you should start searching for machine learning engineering roles with Randstad today.