How to Become a Machine Learning Engineer

If you’ve ever used a chatbot, been recommended a new Netflix series, or wondered how your phone’s voice assistant seems to know exactly what you’re asking, you’ve encountered the work of Machine Learning (ML) Engineers. These professionals are behind some of the most exciting technological developments of the past decade, and demand for their skills continues to grow. For most people, the job of ML Engineers is still a mystery, but that doesn’t have to be the case for you. Interested in learning how to become a Machine Learning Engineer, then?

In this article, we break down exactly what a Machine Learning Engineer does, why ML is such a sought-after career and the technical and soft skills you’ll need to succeed in the role. We also explore how much you can earn working in ML in the UK, the education routes that can lead you there, and how to get into ML engineering from other tech jobs. If you’re interested in this career path or you’re just curious about AI-driven roles, this guide has everything you need to become a Machine Learning Engineer.

What is a Machine Learning Engineer?

A Machine Learning Engineer is a type of software engineer who builds systems that can learn from and make decisions based on data. They design algorithms, train models, and deploy them into real-world environments, essentially turning data into predictive tools.

“You can think of them as the bridge between data science and software engineering.”

David Berwick, Software Engineering Recruitment Specialist

While data scientists often experiment and explore data, ML engineers focus on building scalable, production-ready solutions. For example, a data scientist might identify a useful pattern in customer behaviour, but it’s the ML engineer who takes that pattern and builds the system that personalises recommendations in a mobile app.

What is Machine Learning used for?

Machine learning is used to power all sorts of everyday applications, from fraud detection in banking and personalised shopping recommendations to language translation tools and even predicting equipment failures in manufacturing. In healthcare, ML assists with early diagnosis by analysing medical images. In transport, it’s helping optimise delivery routes and improve driver safety.

Other jobs in Machine Learning besides ML Engineering

Machine learning is a broad field, and several different roles are depending on your skill set and interests. Not everyone working in ML is an ML Engineer! Some of the most common job titles include:

• Data Scientists use ML and statistical methods to analyse data and uncover insights, often creating prototype models.
• AI Researchers work on developing new Machine Learning algorithms or improving existing ones, often in academic or R&D settings.
• Computer Vision Engineers specialise in ML models that interpret visual data, such as facial recognition or autonomous driving.
• Natural Language Processing (NLP) Engineers focus on language-based models used in chatbots, translation tools, and sentiment analysis.

Each of these roles requires slightly different skills and levels of experience, but they all share a foundation in machine learning. You can expect the list of ML job titles to expand in the following years as ML becomes a top skill in digital and tech jobs. This means new opportunities at all levels, from entry-level positions to advanced research roles.

Why are Machine Learning Engineers in high demand in 2025?

Machine learning is being adopted across nearly every industry, from healthcare to finance, retail to logistics. As businesses collect more data, they need intelligent systems to make sense of it and act on it automatically. That’s where ML engineers come in.

According to LinkedIn’s Jobs on the Rise 2025 report, roles involving AI and machine learning are growing at a fast pace, with more companies hiring for positions like Head of AI and AI Consultant. Even smaller companies are now exploring how to automate processes, improve user experience, and cut costs with AI, creating more job opportunities for ML professionals at all levels.

Must learn tech skills to become an ML Engineer

If you want to break into ML engineering, you’ll need a solid foundation in both programming and data science. Here are the essential skills:

• Programming languages: Python is the industry standard, thanks to its simplicity and the strength of its ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Java and C++ can also be useful, depending on the use case.
• Mathematics: A strong understanding of linear algebra, calculus, probability, and statistics is essential for building and optimising ML models.
• Data handling: Knowing how to clean, transform, and analyse large datasets using tools like Pandas, NumPy, or SQL is crucial.
• Model training and evaluation: You’ll need to know how to choose the right model, train it effectively, and evaluate performance using metrics like accuracy, precision, recall, and F1 score.
• Cloud platforms: Familiarity with cloud environments such as AWS, Azure, or Google Cloud is increasingly important for deploying machine learning models at scale.

You don’t actually need to master everything at once to become a Machine Learning Engineer. Many ML engineers start by learning Python and building small projects, then deepen their knowledge through more advanced tools and concepts over time.

The soft skills that will take you further in your Machine Learning career

While technical ability is essential, soft skills often make the difference between a good ML engineer and a great one:

• You need good Communication skills to explain complex technical concepts to non-technical stakeholders, such as product managers or business leaders.
• Problem-solving is vital, too, as Machine learning is often about trial and error. Being comfortable experimenting, learning from mistakes, and trying new approaches is key here.
• ML engineers often work with data scientists, software developers, and product teams. Being able to collaborate effectively with your team is essential.
• Working in a rapidly evolving field, those who are naturally curious and enjoy lifelong learning tend to get further in their ML career path.

Salary prospects for Machine Learning Engineers in the UK

Machine learning engineering is one of the highest-paying roles in tech in 2025, according to data from Glassdoor and our data survey. Naturally, salaries can vary by region, so it’s always worth speaking to a specialist recruiter in your area to make sure your expectations are aligned with the local market.

• Entry-level ML engineers in the UK can expect to earn £35,000–£45,000.
• Mid-level ML roles typically range from £50,000–£70,000.
• Senior and specialised ML roles (e.g. NLP or computer vision experts) can exceed £90,000, especially in London or within top-tier firms.

Do you need a degree to get into ML Engineering?

Most ML engineers have a degree in computer science, maths, physics, or engineering, but it’s not the only route. There are plenty of high-quality machine learning courses and boot camps these days. However, a degree, especially one with a focus on algorithms and statistics, can significantly speed up your learning and open doors with top employers.

There isn’t a specific machine learning engineer learning path, so if a university degree or a master’s degree isn’t an option for you, these alternative routes can help you get your foot into a Machine Learning Engineering job:

• Online courses: Platforms like Coursera, Udemy, and edX offer affordable courses on Python, data science, and ML.
• Bootcamps: Intensive training programmes can provide structured learning and sometimes even job placement support.
• Certifications: Google’s ML Engineer certification or AWS Certified Machine Learning credentials can boost your CV.
• Build a portfolio: Creating real projects, like predicting stock prices, building a recommendation engine, or training a chatbot, can make a big impact on employers.

A strong GitHub profile showcasing practical work often speaks louder than a list of qualifications. Our experience is that most hiring managers will hire an ML Engineer with both title and experience rather than an ML Engineer without an academic background.

Moving into ML from other digital or tech roles?

Already working in tech? Transitioning into ML engineering may be easier than you think if you have some of the related skills ML Engineers have or strong digital or tech experience.

• Software developers already have coding skills, which gives them a huge advantage. If you are a Python developer, then all you need to become a Machine Language Engineer is to learn the maths and ML-specific tools.
• Data analysts or BI professionals are familiar with data wrangling and often use tools like SQL or Python, so the jump to ML seems like a natural progression for them.
• Digital marketers or technical SEO specialists can use their experience with data tools and performance metrics and, learning Python, move into ML-assisted marketing automation roles.

We can help you find a Machine Learning job

Machine learning engineering is one of the most exciting and future-proof careers in tech, and it’s more accessible than many people realise. Whether you’re starting your journey or pivoting from another role, there’s a clear path to follow.

At Adria Solutions, we’ve helped candidates from a variety of backgrounds land their first machine learning roles, and we work with companies across the UK who are actively hiring in this space. If you’re ready to take the next step in your ML career, get in touch. We’d love to help you find your perfect role.