Machine learning engineer: What do they do?

In almost every sector today, technological advances are rapidly changing the landscape. The use of big data and machine learning is revolutionising everything from healthcare and fitness to sport, finance and more.

One of the key figures in many organisations overseeing the change in the workplace is a machine learning engineer – a person responsible for developing and implementing algorithms that enable machines to learn and process and make intelligent decisions. But what exactly does their job entail? And what skills do they need? In this post, we’ll look at the role of a machine learning engineers. And if you are keen on working in this type of job, you should consider studying a master in Big Data Analytics.

Responsibilities of a machine learning engineer

The role of a machine learning engineer is a relatively new concept, brought about because of the advances in how we use data in all aspects of our lives.  A person employed in this role plays a key part in any organisation in developing and using machine learning models and systems.  While there may be some variations in their exact role depending on the sector and organisation they work for, the following tasks give a give oversight of the responsibilities of a machine learning engineer.

• Data collection and preparation: Of all the tasks required, the machine learning engineer is responsible for identifying and collecting relevant data sets, transforming the raw data to ensure its quality for the analysis stage, and collaboration with other data experts on deciding the appropriate data collection methods.
• Model development and training: The machine learning engineer needs to select the appropriate algorithms and techniques to solve the problems presented by the data available. To do so, they’ll need to build, test, and refine models using tools such as TensorFlow or PyTorch. For large scale data sets, they can use tools such as Apache Spark or Hadoop.
• Model evaluation and validation: For the optimal performance of machine learning models, the engineer must continuously test the accuracy and precision by using techniques such as cross-validation and A/B testing.
• Deployment and integration: Once the machine learning models are established and tested, it is the responsibility of the engineer to work with other software engineers and DevOps teams to deploy the models into production environments and ensure they are scalable, reliable, and efficient.

Machine learning engineer skills

To excel in this role, machine learning engineers need a diverse skill set that combines technical expertise, problem-solving abilities, and domain knowledge. Some key skills and qualifications include:

• Proficiency in programming languages such as Python, R, or Java.
• Solid understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Experience with popular machine learning libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, or Keras.
• Strong mathematical and statistical background, including linear algebra, calculus, and probability theory.
• Familiarity with big data technologies, such as Apache Hadoop or Spark.
• Knowledge of software engineering principles and best practices.
• Effective communication and collaboration skills to work with cross-functional teams.

What to study to be a machine learning engineer?

Machine learning engineers are instrumental in harnessing the power of artificial intelligence to drive innovation and solve complex problems. From data collection and preprocessing to model development, evaluation, and deployment, they are involved in every stage of the machine learning lifecycle. If you're passionate about the intersection of technology and data, becoming a machine learning engineer could be a rewarding career choice.

And at Universidad Europea, if you choose our master in Big Data Analytics, or our degree in Computer Engineering, you will gain the skills necessary to succeed in an increasingly competitive and in-demand market. Our academic model is based on experiential learning and throughout your programme you’ll have the chance to take part in work placements and internships at leading companies, working alongside industry experts to gain the latest knowledge and skills within the sector.