Our guide on how to become a machine learning engineer offers a comprehensive look at the role, the industry, and the typical ways that prospective engineers enter the field.
With generative artificial intelligence (AI) fueling a quantum leap in productivity, machine learning engineers are quickly becoming one of the most in-demand IT professionals for all kinds of business operations across all sorts of industries. As AI continues to advance further, companies are going to need to hire more people with a background in and knowledge of machine learning.
Coding, deep learning, computer science, data science, mathematics, and problem solving are all skills that prospective machine learning engineers should be proficient in. However, to truly shine in the industry, you need to be multilingual when it comes to programming languages.
To help give you a better idea of where to begin, we’ll answer some key questions, including:
What does a machine learning engineer do?
What skills do I need to become a machine learning engineer?
What career opportunities are available to machine learning engineers?
Do I need a degree to become a machine learning engineer?
How do I become a machine learning engineer?
Machine learning engineer skills and job requirements
Nevertheless, machine learning engineers are still expected to have a broad skill set, demonstrating versatility while melding their theoretical knowledge with some hands-on experience.
What skills do you need to become a machine learning engineer?
In addition to possessing a fundamental ability to code, machine learning engineers are expected to be proficient in a number of other areas. Here are some you should familiarise yourself with.
Computer science fundamentals and programming: You’ll need to be able to build structures (i.e. stacks), apply algorithms, understand computability and complexity (e.g. P vs. NP, NP-complete problems, approximate algorithms), and develop computer architecture
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 modelling 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
Examples of machine learning engineer jobs
Machine learning is helping businesses across all sorts of industries to be more efficient and to deliver better products and services for their customers. Here are some examples:
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)
Given the fact that AI is a field within technology that’s in its relative infancy, both job security and job opportunities are in abundance and expected to grow over the coming years. It’s nigh on impossible to pinpoint an industry that isn’t exploring new innovations in AI, allowing companies to develop new products quicker and in a more streamlined fashion, while embracing the latest and greatest technology.
That said, with the varying skill sets that are demanded by machine learning engineer roles, there are plenty of opportunities to specialize in one specific field. There is an array of job roles a machine learning engineer can pursue and it may be worth tailoring your education pathway if one seems more attractive than the others.
Quick facts about working as a machine learning engineer:
The average salary for a machine in 2023 is $160,663 in the US and £66,535 in the UK
The demand for AI and machine learning specialists could grow by 40% between 2023 and 2027
The most sought-after degree employers look for when hiring machine learning engineers is computer science
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, analyse, 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 visualisation, test code, create technical specifications, and maintain systems
Human-centred machine learning designer: Create intelligent systems to learn individuals’ preferences and behavior patterns through information processing and pattern recognition
Machine learning engineer duties
The role of a data scientist vs a machine learning engineer closely overlap: they both must work with vast amounts of data, carry out complex modelling on datasets and perform data management. Both must also design software that can automate predictive models.
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Where the two differ is that a machine learning engineer is responsible for building algorithms to improve predictive accuracy and analyse data without the need for human intervention. Machine learning is linked to AI and deep learning, where artificial neural networks use deep data sets to solve complex problems.
A machine learning engineer may be hired by an e-commerce company to improve how recommendations are personalised for individual customers. Or they could be working with a fintech startup to assess whether potential customers are a credit risk or are more likely to default on a loan.
How do you become a machine learning engineer?
Most employers require machine learning engineers that have a strong educational background. Ideally, at least a Master’s degree or even a PhD in a relevant field.
As a starting point, we recommend looking at our list of the best machine learning courses to give you a sense of what's available.
Dedicated degrees in machine learning are a relatively new concept, and so it's common to see machine learning engineers with a background in computer science, electrical engineering, mathematics, physical sciences, and statistics. A number of institutions in the US, including Carnegie Mellon, offer access to machine learning curriculums and modules.
While a master’s or PhD is generally a requirement, it can be difficult to land a job given that machine learning engineering can be an overcrowded market. Bootcamps, run by organisations like Springboard, are designed to fast-track your career progression within a matter of months – graduates from Springboard have gone on to be hired by Google, Microsoft and Netflix.
One thing employers will almost certainly look for when hiring is any hands-on experience. A great way to learn, sharpen your skills and build a portfolio of work is to run test projects on GitHub.
Once you have that foundational knowledge, you should be continuously looking to learn and refresh your knowledge and skills. It’s worth staying up to date with the latest algorithms, programming languages, and machine learning libraries. It’s also important to ensure you have the latest professional certificates.
Once you are comfortable with that, it's time to start your job search. It's important to remember that your career progression is unlikely to be linear, so it's worth considering roles that play to the strengths you developed as part of your learning. Software engineering, programming, development, data science, or even computer engineering are good options if you're looking to develop your skills professionally before taking the leap to machine learning. You may even continue your education while you work.
When looking for machine learning engineering roles, remember to make use of LinkedIn. Connect with like-minded people and machine learning experts – they may be able to share some valuable industry insight into getting your foot in the door. There are also plenty of great LinkedIn courses available if you need to brush up on soft skills like management and communication.
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 organisation 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.
Start your journey toward becoming a machine learning engineer
Machine learning is obviously an exciting industry given the rising demand for machine learning skills and it pays well too, plus, there’s plenty of opportunities to grow once you land your first job.
Now could be the ideal time to kickstart your journey to becoming a machine learning engineer. Decide which jobs interest you most and in which industry. Then use your analytical skills to draw up a plan to launch your career.
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Rich is a freelance journalist writing about business and technology for national, B2B and trade publications. While his specialist areas are digital transformation and leadership and workplace issues, he’s also covered everything from how AI can be used to manage inventory levels during stock shortages to how digital twins can transform healthcare. You can follow Rich on LinkedIn.