How to become a machine learning engineer

What’s involved in being a machine learning engineer and how to become one

Woman holds mobile phone while pointing to a printed graph

Machine learning engineers are some of the most highly sought after professionals in the IT industry, and this will only continue to grow as more companies adopt artificial intelligence (AI) technologies. There are plenty of job opportunities available today, and in the future, organisations will need even more people with machine learning training.

Statistics, data science, computer science, mathematics, problem-solving, and deep learning skills are all necessary for machine learning engineers. In order to succeed, machine learning engineers must learn a number of programming languages and ensure they are precise when working with complicated data sets and algorithms.

If you're looking to start a career as a machine learning engineer there are plenty of online resources to study from. Unfortunately, the huge quantity of information can make absorbing it rather difficult. It can also be a challenge trying to decide which career path is right for you, as there are an enormous number of machine learning opportunities across many different industries.

In this article, we’ll answer some critical questions about becoming a machine learning engineer:

  • What does a machine learning engineer do?
  • What career opportunities are available to machine learning engineers?
  • Do I need a degree to become a machine learning engineer?
  • What skills do I need to become a machine learning engineer?
  • How do I become a machine learning engineer?

What are a machine learning engineer's responsibilities?

data scientist and a machine learning engineer share similar duties: they must carry out complex modelling on dynamic data sets, perform data management, and work with vast amounts of data. Furthermore, they are also expected to design self-running software to automate predictive models, which utilise their previous findings to improve their accuracy in performing operations in the future.

As you may have guessed from reading the title, machine learning engineers work with machine learning, which utilises algorithms to improve predictive accuracy and analyze data without human intervention. Machine learning is linked to AI and deep learning, where artificial neural networks use deep data sets to solve complex problems and "think".

Machine learning has many applications, including:

  • Image and speech recognition (e.g., auto-tagging images, text-to-speech conversions) 
  • Providing customer insights (e.g., noting a customer purchased product 1 and recommending product 2)
  • Risk management and fraud prevention (e.g., financial predictions, risk of loan defaults)
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Quick facts about working as a machine learning engineer

  • The average pay for machine learning engineers in 2020 was $147,134 per year.
  • Job postings for machine learning engineers have grown by 344% from 2015 to 2018.
  • Machine learning engineers typically require a master’s degree or PhD in computer science, software engineering, or a related field for the best career prospects.
  • Most job advertisements in fields involving AI or machine learning are for machine learning engineers.  

What are the career opportunities for machine learning engineers?

There are many career opportunities for machine learning engineers, as there is growing demand in many different industries, including health care, education, retail, manufacturing, supply chain, and logistics. The increase in the use of AI and deep learning across multiple industries will also cause the demand for machine learning engineers to increase. 

As you develop your experience and knowledge in the requisite programming languages and other aspects of the role, you’ll discover new career opportunities as a machine learning engineer.

Types of machine learning engineer jobs:

  • Machine learning engineer: Use of machine learning algorithms and tools to design and develop systems and applications
  • Data scientist: Use big data, AI, machine learning, and analytical tools to collect, process, analyze, and interpret large amounts of data
  • NLP scientist: Design and develop machines and applications to learn human speech patterns and translate spoken words into other languages
  • Software developer/engineer: Design, develop, and install machine language software solutions, create computer functions, prepare product documentation for visualization, test code, create technical specifications, and maintain systems
  • Human-centered machine learning designer: Create intelligent systems to learn individuals’ preferences and behavior patterns through information processing and pattern recognition

Education needed to be a machine learning engineer

Most employers will only employ machine learning engineers with at least a master’s degree in computer science, math, statistics, or a related area. 

Going through a master’s degree program will provide programming knowledge (e.g., Python, R, Java), understanding of machine learning frameworks (e.g., TensorFlow, Keras), and advanced mathematics skills (e.g., linear algebra, Bayesian statistics). 

Professional certification from Amazon or an accredited association will also help you to stand out in the field.

Skills you need to become a machine learning engineer

To become a machine learning engineer, you’ll need to learn one or more programming languages, such as Python, Java, and R. You might also need to learn C++, C, JavaScript, Scala, and Julia. You’ll also need to develop skills through a combination of education and on-the-job experience.

  • Computer science fundamentals and programming: Build data structures (e.g., stacks, queues, multi-dimensional arrays), apply algorithms (e.g., searching, sorting, optimization), understand computability and complexity (e.g., P vs. NP, NP-complete problems, approximate algorithms), and develop computer architecture (e.g., memory, cache, bandwidth).
  • Probability and statistics: Employ techniques used in probability (e.g., Bayes Nets, Markov Decision Processes, Hidden Markov Models), calculate statistical measures and distributions (e.g., uniform, normal, binomial), and apply analytical methods (e.g., ANOVA, hypothesis testing) for building and validating models from observed data.  
  • Data modeling and evaluation: Estimate the underlying structure of a given dataset, find useful patterns (e.g., correlations, clusters), predict properties of unseen instances (e.g., classification, regression), choose appropriate accuracy/error measures (e.g., log-loss for classification, sum-of-squared-errors for regression), and evaluate strategies (e.g., training-testing split, sequential vs. randomized cross-validation).
  • Machine learning algorithms and libraries: Find suitable models to apply libraries, packages, and APIs (e.g., Spark MLlib, TensorFlow), create learning procedures to fit the data (e.g., linear regression, gradient descent, genetic algorithms), and develop an awareness of advantages and disadvantages of different approaches (e.g., bias and variance, missing data, data leakage).
  • Software engineering and system design: Understand how elements work together, communicate with systems (e.g., library calls, database queries), and build interfaces.

How to become a machine learning engineer

It’s best to develop a strategy before applying for a position as a machine learning engineer. Determine the industry you want to work in and what type of machine learning engineer you’d like to be.

Once you have a relevant undergraduate degree, you might want to get a position with a career path leading toward becoming a machine learning engineer. This could include working as a software engineer, programmer or developer, data scientist, or computer engineer. 

While you’re working in one of these careers, you can study for a master’s degree or PhD in computer science or software engineering. 

Make sure to stay on top of current algorithms, programming languages, and machine learning libraries. Take continuing education courses and update your professional certifications.

Build your network and learn more about the role by connecting with other machine learning engineers on LinkedIn, which will keep you in the know on job openings and industry expectations. Ask your contacts for advice on building your career as a machine learning engineer.

Tips for applying to machine learning engineering jobs

  • Update your knowledge, skills, and certifications on your resume before applying for a job. Highlight the skills advertised in job postings and list your previous accomplishments.
  • Write a cover letter to explain how your experience makes you ideal for the role. Describe why you want to work with the organization and why they should hire you.
  • Include relevant references, but ask them for permission first and ensure their contact information is correct.
  • Search job boards that post machine learning engineer jobs. 

Starting your journey toward becoming a machine learning engineer

Machine learning offers many opportunities for potential careers, and people in this field earn high wages and have a solid future. Now is a great time to start working toward a career as a machine learning engineer. 

Find out what jobs would most interest you and what roles are available in your desired field, as well as what skills and experience are required. Consider using your skills to analyze the data and formulate a plan to launch your career as a machine learning engineer.

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