Machine Learning Engineer

Machine Learning Engineer Job Profile

What Is a Machine Learning Engineer?

A Machine Learning Engineer contributes to the development, implementation, and optimisation of machine learning systems — working in teams with data scientists, programmers, and other IT professionals.

Machine learning is one of the megatrends of the current technological era. It involves the ability of computer systems to independently analyse large datasets, recognise patterns, and make predictions based on them. ML is a subset of Artificial Intelligence and is delivering value across a growing number of use cases — from train occupancy forecasting to fraud detection and medical diagnostics.

ML Engineers act as teachers: they train ML software solutions so that systems can learn autonomously and continuously improve. Without their expertise, this technology would not be possible.

What Does a Machine Learning Engineer Do?

ML Engineers focus on building the technical foundation for machine learning. Their work spans four key areas:

• Building data structures — creating the data foundations on which machine learning operates
• Programming ML solutions — writing software that independently evaluates available data
• Developing algorithms — designing the learning logic behind ML systems
• Maintaining and monitoring — supporting, controlling, and optimising deployed ML systems

In larger teams, ML Engineers may focus exclusively on maintenance and optimisation. In smaller organisations, they often also take on tasks that overlap with data analyst and data scientist roles — though the boundaries between these roles remain fluid.

How Does a Machine Learning Engineer Differ From a Data Scientist?

A Data Scientist focuses extensively on data analysis and data visualisation. Proficiency in statistics is particularly important in this role. A Machine Learning Engineer, by contrast, requires excellent programming skills and focuses on implementing ML systems directly — identifying areas for optimisation and proposing solutions. They work with the systems themselves, not just the data.

What Tools Does a Machine Learning Engineer Use?

Job and project specifications typically require knowledge in:

• Java — widely used for production ML systems
• Python — dominant language for ML model development and data pipelines
• Scala — used for distributed data processing, particularly with Apache Spark
• Workflow automation tools — for orchestrating and scheduling ML pipelines

The Required Profile Of A Machine Learning Engineer

An ML Engineer should possess various hard and soft skills. Among the hard skills, appropriate education is important. Many ML specialists have completed the following degree programs:

• Computer Science or Business Informatics
• Mathematics or Applied Mathematics
• Engineering sciences

Basically, even career changers can earn money as a Machine Learning Engineer, especially as freelancers who have good chances of starting a career. It is important that they have acquired sufficient expertise in fields such as computer science and statistics. Additionally, they should be familiar with tasks such as programming. For those who are unsure, they can, for example, buy a specialized book on Machine Learning or the corresponding profession: Interested individuals will quickly realize through reading whether they are up for this challenge.

In terms of soft skills, communicative abilities, English language proficiency, willingness to learn, and flexibility are important. ML engineers should be able to collaborate seamlessly with other specialists such as data scientists. The development of AI systems is teamwork! Specifically, freelancers should also be able to adapt to new projects and work situations. Good knowledge of the English language is indispensable in this field of work!

How Much Does A Machine Learning Engineer Earn?

In the case of ML engineers, like in all IT professions, there is a wide range of earning possibilities. In general, these specialists can expect an attractive income, whether they are employed full-time or working as freelancers. The reason for these positive earning prospects is evident: machine learning and AI systems have tremendous potential, accelerating digitization processes to an unprecedented extent. Therefore, software developers and users are investing heavily in this field, desperately seeking experts and willing to pay a premium for their support.

Full-time machine learning engineers can expect a starting salary of around 50,000 euros. However, this figure serves as a rough guideline, as it depends on various factors. The same applies to freelancers. The following aspects, among others, influence the fixed salary or compensation for projects performed by self-employed individuals:

• Qualifications of the ML engineer
• Size and financial strength of the employer or client
• Job or project description
• Negotiating skills of the employee or freelancer

For instance, ML engineers with a master’s degree have better income prospects compared to those with a bachelor’s degree or career changers. Generally, larger companies tend to pay more than small and medium-sized enterprises (SMEs), but there are exceptions depending on specific circumstances. The ML and AI market is populated by many startups, some of which have received substantial investments. These startups also offer high salaries and compensations.