Have you ever thought about what a machine learning engineer really is and what they do? In this article, you will learn every aspect of the job from getting into it, to retirement. You will also come to know what they do on a day to day basis and how much they get paid.
Machine learning engineers also known as an ML engineers sit at the core of modern technology which is revolving around AI systems. Since AI is rapidly growing, these specialists must continuously learn new technologies and advancements in the tech space.
They typically create programs that can transform a heavy amount of data into practical features for apps. For example, streaming platforms, like Netflix, that recommends TV series accurately to your preferences is all thanks to an ML engineer who developed the system behind it.
Day to day, a machine learning engineer aggregates data, cleans the data, to allow computers to learn from it to finally make accurate predictions consistently without needing reprogramming for every new situation. They mostly spend time testing the models they've built to assure reliable functionality.
Machine learning engineers are employed in any field that requires well-built intelligent systems. This includes, health, security, research and science, and more.
To become a machine learning engineer, it's essential that you build these skills: python programming, mathematics and statistics, machine learning algorithms, data engineering, MLOps tools, cloud platforms, software engineering's best practices and last but not least, communication. Usually, an ML engineer would start their career as a software engineer or data scientist. In this way, they master half of the needed competences via higher education and work and the second half via self-teaching.
ML engineers salary depends on progress in your career path. A junior would get around $95,000 to $130,000. A full fledged ML engineer would receive about $130,000 to $157,000. Finally, a senior in the field would enjoy near $157,000 to $230,000. The salary mostly depends on the company in need.
Machine learning engineers usually need to graduate with CS, statistics, or tech backgrounds. While employers care more about an individual's ability to build models than a degree, a degree would show that you have a background in the field.
ML engineers do not work in physically dangerous environments like construction sites. However, they still face risks such as being liable whenever a system model fails. Because models can be unpredictable sometimes, errors can result in severe real world damages.
The chances that ML engineers have of getting replaced by robots or AI are very low since they are building AI. Unlike an AI engineer who uses already built Artificial Intelligences to deploy in the company, a ML engineer builds the AI from scratch to make it serve a specific purpose. Unless AIs achieve a state where they can reproduce themselves, this job is safe from being taken over.
Since career paths as a machine learning engineer is still relatively new, it's difficult to say when they could retire. However, traditionally, engineers in other disciplines retire from ages 60-65 which could potentially estimate a ML engineers end of career.
In my case, I'm just starting to walk on the path to ML engineering. There are still many things I must learn before achieving this goal, such as mastering Python, cleaning unstructured data, and more.
Rationale: to inform or educate
Authority: research journal
Date: recently published
Accuracy: probably true
Relevance: relevant for this document
Sources: cites sources
Rationale: to inform or educate
Date: still relevant
Accuracy: probably true
Relevance: relevant for this document
Sources: cites sources