What Do Machine Learning Engineers Do? Roles & Impact 2025
- cvguys.in
- Jul 9
- 11 min read
Updated: Jul 10
So, What Exactly Is a Machine Learning Engineer?
Envision a world where computers are not just the best command-followers you've ever seen, but can also learn by experience - they can stop fraud before it happens, suggest the next great show for you to watch, or support doctors who can diagnose disease faster than ever. Welcome to the world of the machine learning engineer, often invisible but essential, and the catalyst for many of the technology solutions we take for granted.
The machine learning engineer operates between the disciplines of software engineering and data science, taking the ocean of unstructured information provided by the advents of big data and using it to build new, intelligent systems. However, the role of the machine learning engineer is not just an exercise in mathematical possibility but a necessary foundation for competitive business.
Without a machine learning engineer, businesses would be left guessing, forever behind in finding previously unseen trends, opportunities, and efficiencies made clear by a well-crafted (built) algorithm. In fact, over 74% of respondents to a recent survey consider machine learning the biggest game-changer and 65% of businesses are now using machine learning to enhance their decision-making and productivity.
The demand for these professionals is soaring. As of 2025, the global machine learning engineering market is worth an astounding $113.1 billion and is made up of almost 1.6 million jobs worldwide—a leap of over 219,000 new jobs in just the last year. For context, if you're at all worried about job security, here is a stat to think about: LinkedIn rated machine learning engineer as the fourth fastest growing job title in the United States, with averages salaries between $72,600 and $170,000, and a median experience requirement of only 4 years.
But it wouldn't be fair to think that this is only about a good income and good job growth. Instead, the real value is in the impact. In health care to finances, retail to logistics, machine learning engineers are building smarter, faster, and more responsive systems for all aspects of our lives. For example, the next time your streaming application seems to read your mind, remember: it isn't magic; it's a machine learning engineer.
The Many Hats of a Machine Learning Engineer
If you think the job of a machine learning engineer is about sitting in a darkened room tapping away at a keyboard with a cup of coffee in hand, you are sorely mistaken. These individuals really know how to multitask—between programming and building software, collaborating with other teams, and sometimes even playing the role of detective when data are missing.
Machine learning engineers are primarily responsible for designing and building the intelligent systems that make everything from voice assistants to fraud detection possible. Their to-do list is impressive in its variety; in one minute they are selecting appropriate data sets and ensuring that the data is cleansed, in the next they are taking a prototype and turning it into something that can be scaled, and then they are retraining models to keep pace with trends. It is now estimated that 56% of organizations are using machine learning in at least one business function, so their role is not only important, but it is also evolving.
However, technical skill isn’t everything. Skills in programming languages like Python or R matter, as do knowledge in statistics, algorithms, and big data technologies. Soft skills are also hugely important. In a world where remote and hybrid jobs are now commonplace—where communication and collaboration are now essential and not optional benefits of a job—ML engineers will need to blend into teams that are situated around the world. A recent survey found that 98% of remote workers want to keep working remotely at least part of the time.
So, what hat would you wear best? A coder’s hat? An analyst’s visor? A collaborator’s beret? A machine learning engineer needs to wear all of them at once, in fact, I have even seen outlines of their responsibilities being labelled both analyst and collaborator in the same job description and the same meeting. If you like variety and new puzzles every day, you will feel right at home in this rapidly changing field.
Data Wrangling – Where the Magic (and Mess) Begins
Before a machine learning engineer can build anything even remotely intelligent, they are first faced with the wild beast that is raw data. Contrary to the picture of engineers developing imaginary models, the process is much less glamorous, and more likely filled with the feeling of wrestling with spreadsheets that have survived a tornado!
In reality, data preprocessing and cleaning can take up to 80% of a data practitioner’s process, and if you thought this job was exclusively about algorithms and neural networks, SURPRISE! Spending the majority of your time pre-processing messy, inconsistent and incomplete datasets is a reality.
As you may have deduced, this is very important. The quality of your model is directly affected by the raw data you're working with. Raw data is seldom in a form that can just be taken and ran; it almost always has some sort of missing values, duplicates, outliers and features that should not have been included.
Imagine trying to teach a robot how to make coffee from a recipe, that was written in five languages, and then half of the steps were missing and the other half were in the wrong order! That's how unprocessed data feels like to the data practitioner.
When it comes to the power of data wrangling, the potential impact is significant. Preprocessing steps, such as addressing missing values, normalizing the data, and encoding data, can lead to improvements in model accuracy, faster model training, and a reduced risk of overfitting.
Machine learning adoption is already accelerating globally, and the deep learning expert market is expected to develop into a $113.1 billion industry by 2025, so there are more job opportunities than ever for engineers with data cleaning skills.
So, if you enjoy bringing order to chaos and love a little detective work, then data wrangling is the solution for you! Remember: behind every brilliant algorithm, there’s an engineer that wasn’t afraid to work their magic on the mess!
Model Building – The Art and Science of Teaching Machines
Once the data dust settles and the machine learning journey is underway, machine learning engineers enter the most creative, and sometimes maddening, phase of the process: model building. It's here where the actual teaching begins, if we can call it that. Think of model building as a recipe from scratch, with some mathematics, some algorithms, and a whole lot of intuition. The machine learning engineer's intent is to allow the machine to learn patterns, make predictions, and (hopefully) not embarrass yourself in front of your boss or other stakeholders.
Given that there is an art to model building, there is equally a science. The machine learning engineer has to decide which feature(s) (the most relevant pieces of information) to draw on, which algorithm(s) to work with, and how to design a model to detect patterns in the data. It certainly isn’t as simple as putting numbers into a black box. Every consideration a machine learning engineer makes — what features to include, what model to use, hyperparameter tuning — could make or break the final results.
It’s no surprise that companies are compelled to pay top-dollar compensation packages for this level of expertise: in the United States, machine learning engineers can expect to earn an average salary of $162,509 by 2025 — more than double the national average — while in India, data scientists can earn anywhere from ₹6 to ₹29 lakh per year, depending on their level of experience and the company they work for.
However, let’s not forget about the big paychecks. Model building is a lot of trial and error. You’ll easily spend a few days tuning hyper-parameters and then the model will not improve by a tiny fraction. But when it finally works, there is no feeling like it—almost like you, blindfolded, did a Rubik’s cube. Particularly, the number of job postings for machine learning positions has increased 75% year to year over the past five years. So obviously this skill is red hot right now.
So if you're willing to experiment, make mistakes, and talk to your computer sometimes (no judgement), model building may be for you.
Testing, Validation, and the Perils of Overfitting
After the model is built, the machine learning engineer reaches a critical checkpoint: testing and validation. In general, this isn't just running some numbers on a screen and saying you are done. It's determining how well this model is going to work on new, unseen data, as well as the data on which it has been trained.
One of the danger-zone points is overfitting, which means a model fits the training data perfectly (such that it would likely also pass a similar practice test), but fails the real-world final exam. In terms of education, think of it as a student who has memorized an entire textbook. He breezes through every practice exam until a surprise question caused him to freeze, can he apply the learning to analyze the new data?
Research has shown that while about 85% of organizations conducted capable implementers of ML, only about 20% of those organizations successfully implement the models there training. For some Organizations, the validations and performance issues derailed deployment of the models. 64% of organizations take a month or longer to even deploy a model, and also noted that the average data scientist spend about 38% of their efforts prior to deployment or troubleshooting. All organizations need to be cognizant that strong testing, validation, and monitoring is imperative in ML and these techniques need to be built into the project and the organization's DNA.
To find ways of avoiding a stumbling block of overfitting (with no punitive measures), engineers can use techniques: cross validation, regularization, and monitoring while automatically re-pipe-lining and retraining. Automatic pipelining allows the use of various pipelines to retrain and validate new data as sensed data flows.
This provides testing capability quickly in production, with regard for reliability and relevance. Finally, testing, validation and monitoring provide a data-driven and empirical appraisal in order to assess the model established a level of relevancy, or goes back to the beginning.
Deployment – From Lab to Real World
Deploying a machine learning (ML) model represents the moment when everything is brought together. The months of wrangling data and tuning models finally culminate in reality - either in the form of progress or a series of real-world headaches (sometimes it’s hard to tell them apart). Despite the hype, only 32% of ML projects make it to deployment, and the number drops to 22% when looking at transformational initiatives. The rest sit (figuratively speaking) as very expensive, but for now unused Ferraris in the garage.
Why is deployment so challenging? There are many challenges. Performance can be different in production settings, there are always issues related to increased data and scaling, and integration with existing systems is rarely easy. In 2025, companies are expecting robust, reliable and secure deployment solutions delivered in real-time and at production scale - all of which will make this phase of work clearer but also more complex and strategic.
The latest and greatest deployment methodologies and technologies (such as automated monitoring, containerization, and CI/CD pipelines) are now easily accessible for keeping models running smoothly at scale.
However, when basically done well with care and thoughtfulness - deployment is what moves machine learning from an expensive science project to a business value creator (e.g., actionable insights, automated decision-making, and realized value). And, as many experienced ML engineers know, as exciting as being creative in the lab is, the value from a model often occurs after it leaves the lab.
Continuous Learning, Optimization, and Scaling
Machine learning engineers understand that model deployment is not the finish line, but the starting line of a marathon. As we head to 2025 with the total global machine learning market projected to be $113.1 billion, the need for continuous learning and optimizations has never been more important.
Models need to be flexible to new data, changing business requirements, and constantly changing real-world conditions. This is especially relevant when looking at industries like manufacturing, where predictive maintenance powered by AI can reduce unplanned downtime by as much as 50% and decrease maintenance costs by as much as 30%.
Retraining models, tuning algorithms, the performance of models, and making sure that these all continue to scale are a part of typical workflows for engineers. In many instances the real-time data that is collected and analyzed by IoT sensors feed the model continuous new learning which allows the model to begin to predict when things will start to go wrong before they become catastrophic and expensive mistakes for the businesses of the world.
As AI becomes more prevalent in business processes and the increased scale with many sources of data, budgets and potential cloud infrastructure increasing, and many instances where the service is global—all of this contributes to increased complexity and the need to still be assured that various models will continue to perform reliably.
This means that ML engineers find themselves stuck in a continuous cycle of learning and improvements for both their models and themselves. Progress and change are really celebrated in this industry. If you enjoy resolving puzzles as they appear — which is your focus — and you believe that "good enough" is never enough, then the world of continuous optimization will feel very comfortable.
The Human Side – Communication, Collaboration, and Impact
We all know there's a human story behind powerful algorithms and predictive models. One of the most recognized human stories is that of the machine learning engineer; as a technical person, a machine learning engineer is seen as a code wizard, but the reality is machine learning engineers serve many roles: they are communicators, collaborators, and moral actors. The machine learning engineering workforce will be worth approximately USD 113.1 billion in 2025 and employ nearly 1.6 million people globally, and with over 219,000 of those roles created in the last year, the demand is clear. Simply put, the story of machine learning, like any story, is about people.
Organizations expect machine learning engineers to do more than analyze data and build models. Machine learning engineers are expected to wow their audiences by creating memorable engagements where technical (i.e., data) realities are made applicable to relevant (i.e., business) decisions.
Similarly, machine learning engineers are expected to work collaboratively across the organization with business stakeholders to make sure their model is fair and their training datasets are just. Amazingly, 57.7% of machine learning engineering job postings focus on hiring machine learning engineers with deep knowledge in particular areas.
This means organizations are looking for machine learning engineers who can translate between the language of technology, and the language of business. Furthermore, in a time when remote staffing and hybrid work are rising in popularity, 98% of remote staff want to stay flexible in terms of where and when they work. This points to the need for good communication and collaboration skills.
ML engineers have an influence that reach can far outside the office. Their creations transform the way we bank, shop, receive health care, and even how we engage with the world around us. As machine learning becomes intertwined in our everyday life, the ethical considerations must be taken seriously.
Engineers must be aware of issues like bias and privacy, and we also have to consider the impact of contributors to society. This is also reflected in career progression: 80% of professionals that reskilled in AI and ML received a promotion or new position, 66% of them went into a management/leadership role as a result. This indicates that the field values empathy, vision, and use of brand new tools, alongside technical proficiency.
Thus, as you contemplate a career in ML, ask yourself: are you prepared to be the translator for algorithms to impact? Can you assistance teams to unite around a common goal, and still see the implications of your work more broadly? In a world increasingly full of smarter machines, it's the human element that will differentiate you.
Remember, even the most intricate model still always needs a person to make sense of its peculiarities—and sometimes, just needs someone to unplug it, and plug it back into the wall.
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Disclaimer – This post is intended for informative purposes only, and the names of companies and brands used, if any, in this blog are only for reference. Please refer our terms and conditions for more info. Images credit: Freepik, AI tools.
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