How to become a machine learning engineer
Role guide and learning paths
Demand for machine learning engineers who can deliver projects on time and on budget is growing. Companies understand the benefits of adding robust machine learning capabilities to their workflows, but don't always have the people to deploy them.
That's where a machine learning engineer can help. Their role is to build and maintain systems, iterate on existing processes and improve them, while providing support after deployment.
Machine learning engineer in a nutshell
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The role: building, deploying, and maintaining various AI/ML systems that serve users' business workflows and operational constraints.
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Role context: somewhere between software engineer and DevOps.
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Salary expectations: around £75,000 a year (the UK average) and slightly more in London (£500/day in some instances).
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Demand: one of the highest-demand jobs, with up to 180,000 openings, according to PwC's 2026 AI jobs parameter.
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Core skills required: SQL, TensorFlow, Python and PyTorch
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Key job search differentiator: ability to work within existing companies' AI models
What does a Machine Learning Engineer do?
Machine learning engineers are involved in taking existing company data/intelligence and creating feedback loops that allow the firm to iterate on existing processes and improve them. They decide whether AI is the right tool and then define a measurable, beneficial business outcome to work toward.
After that point, most of the job looks a lot like software engineering. The goal is to collect and transform data, train and evaluate models, and relate them to underlying business workflows and processes. This differentiates a machine learning engineer from conventional UI and UX roles, which tend to be user-facing.
It's worth noting a machine learning engineer's role doesn't end at launch. Because they architect the systems that manage underlying company data, their scope increases to additional domains like fine-tuning, establishing ethical guardrails, and model evaluation and analytics.
Why do businesses need Machine learning engineers?
According to RAND's root cause analysis, around 80% of business-based AI projects fail, which is roughly twice the rate of standard IT projects. This often comes down to a mismatch between boardroom ambition and the capabilities of the real people doing the work on the ground. Most brands now have an AI strategy, but only a small minority gets deployments operating dependably.
Businesses need machine learning engineers because of changing bottlenecks in their workflows. AI models are becoming increasingly commoditised, and the challenging part for many companies is to know where to embed them and how to maintain safe operations.
The shortage of machine learning engineering talent is significant, especially in the UK. The Manpower Group's 2026 research report found that AI skills are the hardest of all to source, and 72% of UK organisations are struggling to fill roles.
For those looking for work in this area, the upside is significant. Many companies are prepared to pay a premium, especially if machine learning engineers can deliver genuine productivity gains across departments and organisations.
How to become a machine learning engineer
There are several paths to becoming a machine learning engineer.
For many, the door into this type of work is apprenticeships and vocational training. Hiring for AI roles has risen significantly over the last five years, making it a plausible career path for new starters and career changers.
Around three-quarters of organisations hire machine learning engineers who have previously been in other AI roles or roles related to software and IT. Data analysts, data engineers, and anybody with an understanding of underlying code languages is a potential candidate.
Finally, some people take the academic route into this career, especially those wanting to focus on high-level and strategic work. Many organisations look for graduates with computer science, data science, maths, or statistics qualifications, particularly at the master's level.
The top skills Machine Learning Engineers need
Machine learning engineers need the following skills if they're going to be effective in their roles:
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ML and statistical foundations - including knowledge of confidence intervals, concepts such as consistency and efficiency, the law of large numbers, the central limit theorem, and so on.
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Software engineering discipline - especially in areas like code review, version control, and CI/CD.
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Experience with agentic AI - including model fine-tuning and establishing guardrails.
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Data engineering - including quality control and governance.
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Programming and data handling - particularly Python and SQL.
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Commercial communication - the ability to communicate non-technically with other employees and team members.
You learn more about the best courses for machine learning engineers here.
Machine learning courses and apprenticeship programmes
Machine learning engineer frequently asked questions
What is machine learning?
Machine learning is a type of AI that can learn from general patterns in data rather than having to follow specific programmed rules. It's the technology that first enabled Google’s AI to tell the difference between dogs and cats in photos on websites across the internet. Strict programmes find it challenging to do that, but the machine learning approach using neural networks and backward propagation can do so reliably.
What is the difference between a machine learning engineer and an AI engineer?
The difference between machine learning engineers and AI engineers is minimal. In many companies, their titles overlap. If we were to be strict, we would say that a machine learning engineer operates and trains models on an organisation's own data while AI engineers tend to work more on existing foundation models provided by third-party vendors.
Those applying to machine learning engineer roles should bear this in mind. Some companies may be looking for the latter when advertising the former.
What tools do machine learning engineers use?
Machine learning engineers use a variety of tools, including PyTorch, TensorFlow, Spark SQL, Python, and more. Generally speaking, Python is the most common language used for production work, centring on Kubernetes, Docker, and various model registries or orchestrators like Airflow. Knowledge of various cloud platforms, as well as machine learning and data infrastructure, can also be helpful.
What qualifications do I need to become a machine learning engineer?
There's no single qualification required to become a machine learning engineer. Firms hiring for this role look for competence across several domains. A background in computer science, data science, and maths is helpful, but you can also move horizontally from another software or AI-based role, or even find an AI apprenticeship.
What is the expected salary for a machine learning engineer?
Most machine learning engineers can expect to earn around £85,000 a year outside of London, and perhaps even more than £100,000 a year in London when working for high-growth tech firms.
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