What is a Machine Learning Engineer? How to become one?

What is a Machine Learning Engineer? How to become one?

A Machine Learning Engineer is someone who can create or program self-learning data models efficiently using appropriate research and techniques. They create data models that generate descriptive, predictive, and prescriptive results to solve specific business problems. These models are made to find patterns in massive data sets to arrive at a decision when presented with new data.

Understanding how data works is imperative to becoming a Machine Learning Engineer. With sound knowledge of programming languages to create premises and manipulate data, one must have an insight into business processes to be able to consider all variables in a problem-solving approach. Machine learning engineers possess soft skills as well. They enquire about problems and communicate solutions with ease.

What is Machine Learning?

The field of Machine Learning comes under the umbrella term Artificial Intelligence. Machine Learning uses historical data to find patterns and produce results that help businesses make critical decisions with proven accuracy. Machine Learning models are based on statistical algorithms; regression, decision trees, K-means, and random forests are a few commonly used algorithms. These algorithms can be classified into four categories:

  • Supervised machine learning is when you introduce input or independent variables and output or dependent variables to learn about the effect of independent variables on the output. It involves an iterative teaching process by removing or adding variables and correcting predictions through slight changes in the model. Examples of this category are regression and classification algorithms.
  • Unsupervised technique of machine learning is used in exploratory analysis. When it is not structured into fields or columns, it is challenging to deal with data. Therefore, the model can find patterns in the data in this technique independently. This category includes clustering and association algorithms, where one output form can be a word cloud.
  • Semi-supervised machine learning is not as costly as supervised ML. At the same time, it does not have a minimal application, as is the case for unsupervised ML. The technique uses small amounts of labeled data and large amounts of unlabeled data to create a cost-effective machine-learning model with better accuracy. Speech analysis and Internet content classification are examples of this technique.
  • In reinforcement learning, the training data set is absent, and we have a learning agent and a reward. Here, the model must find the most favorable path for the agent to reach the reward, reacting to and learning from its environment. Optimization is one such technique that falls under this category.

Following are the steps to create and evaluate a Machine Learning model:

  • Data collection: Raw data is collected from relevant sources, which can be in any form (numbers, words, images, audio, video, etc.)
  • Cleaning the data: Data is sorted, and structural errors are fixed. Unwanted outliers are removed, and missing data is either randomly generated, or the entire field is removed to avoid errors in later stages of the process.
  • Split data into training and testing sets: Data is randomly split into a preferable ratio of 80:20 for training and testing sets. Most of the time, the higher the volume of training data, the higher the model’s accuracy.
  • Creating the model: There are numerous algorithms on which a model can be based. The algorithm selection dictates the pattern the computer should look for in the data. Regression, decision trees, and random forests are a few examples of machine learning algorithms.
  • Training the model: Using predefined functions from the programming language in use, training data is provided to train the model.
  • Making predictions: Testing data is used to make a few predictions on the trained model.
  • Evaluate predictions and improve model accuracy: Statistical formulas like R squared are used to measure the model’s accuracy. Based on the results, changes are then made to evolve an even more accurate model until the results are in an acceptable range.

Machine Learning has applications in self-driving cars, forecasting stock market trends, language processing, robotics, vision processing, and many more fields.

How to Become a Machine Learning Engineer?

One can become a machine learning engineer just by learning a few programming languages and acquiring knowledge of their application in the real world. A formal degree is not compulsory for a successful career in machine learning. But the following are the topics that an institution would teach for a learner to excel as a machine learning engineer:

Data structures – are layouts in which data can be arranged. A user would arrange and store data in a format suitable to their needs. Data can be structured into arrays, strings, linked lists, stacks, queues, binary trees, heaps, graphs, etc. It is vital to understand the types of structures as they affect the execution time of a program.

Databases – are clouds where raw and processed data is stored. There are relational and non-relational databases, and they store data in different formats. A relational database would keep records in tables where fields or columns can be associated with one another. At the same time, non-relational databases can store data in document, key-value, column-oriented, and graph formats. These databases complement each other with uses in different business scenarios or industries.

Programming languages – are predefined syntaxes to communicate with a computer. A machine learning professional needs to be skilled in a few languages, including Python, R programming, Java, SQL, etc. At the same time, they should know about the vast libraries of functions or APIs in these languages to be efficient.

Machine learning algorithms – are methods that a machine learning model would apply to arrive at a solution or generate results. A machine learning engineer should be aware of all the algorithms to use the most suitable one to optimize a project’s efficacy and budget. Linear and logistic regression, classification, decision trees, random forests, and optimization are examples of ML algorithms.

What does an ML Engineer do?

ML Engineers, when presented with a problem statement, attempt to solve it by creating a data model representing the business process at play. An ML engineer is required to do the following in an organization:

  • Discuss business problems that an organization might face at any point in time. In an organization, ML engineers form a part of the ‘Data and Insights’ group, and they can be approached to find insights (using data) that evade the human eye.
  • ML engineers study the latest developments in the data science industry. They keep themselves up-to-date with the latest technology and methods applied in the field.
  • They are familiar with a majority of the resources for research either on the internet or in data journals. Resources can be free and paid for; an ML engineer needs to be vigilant of their choice depending on the budget for a particular project.
  • When approached with a problem, ML engineers design the complete plan of research. They collect data and develop one or a combination of supervised, unsupervised, semi-supervised, and reinforcement Machine Learning models based on the needs of the stakeholders.
  • An ML engineer will train the model by feeding clean data into it. The addition of dependent and independent variables is subject to the applied algorithm. They test the model’s accuracy and, in the process, find variables that affect a change in the dependent variable. They remove the least effective variables to improve the model.
  • The results from running tests on the model generate information that is not always readable by non-technical members of the organization. Hence, ML engineers use data visualization to create impactful, easy-to-read graphs.
  • There can be numerous applications for a program, and it is the ML engineers’ responsibility to find new and innovative applications of models that already exist on open-source platforms or in the organization’s library.
  • Managing libraries can be another task for ML engineers. With the knowledge of all the resources at their service, an ML engineer can be more efficient. They may use readily available tools or models instead of creating one from scratch, significantly reducing effort.

What are the roles and responsibilities of an ML Engineer?

Machine-learning engineers work closely with Machine-learning scientists, data scientists, and data engineers to address business challenges.

ML engineers’ responsibility depends on their vertical in a company. They help track key performance indicators in all existing verticals for an organization. Following are a few examples:

Finance – Machine-learning models are integrated into a firm’s Enterprise Resource Planning (ERP) systems. An exhaustive list of financial KPIs like current ratio, gross margin, net profit margin, earnings before interest tax depreciation and amortization (EBITDA) margin, asset turnover ratio, return on capital employed (ROCE), return on equity (ROE), etc., are recorded at frequent intervals to be fed into ML models. Finally, they generate real-time insight into the business’s financial performance, providing leaders with vital information to make critical decisions.

Operations – Firms that manufacture have sensors to record data, and service-based firms have performance management systems (PMS) to access KPIs related to operations. An ML engineer would use this data to share the operational functioning of the organization with the concerned team. Leadership in collaboration with the operations vertical would make vital changes by adding capacity or removing inefficient resources to eliminate bottlenecks that hinder the organization’s growth.

Marketing – There is a wide array of applications for machine learning in marketing. Organizations collect data from their websites and often buy data from companies like Meta, Google, and Amazon to find insights into their target audience with the help of ML engineers. With the advent of machine learning, ads have changed a lot, and the information generated by ML engineers guides the marketing teams to create relevant campaigns. Earlier, the ads would target the masses to filter out prospective customers. More recently, ads have been customized to the taste of individuals creating a more powerful brand image in their minds. Retargeting is when a person is targeted with ads based on their recent online searches. It is one example of how powerful machine learning can be in creating experiences based on individual choices. Netflix’s recommendation system is another excellent example of a powerful machine-learning model.

Sales – It is a measurement of return on investment in marketing campaigns. Earlier, there was no way of knowing if a marketing campaign was successful. Businesses would struggle to find data on what motivated a customer to buy the company’s products or services. But today, an ML engineer working with the sales team can generate insights into factors that motivate sales at microscopic levels. With this information, firms can optimize funding campaigns with a positive outlook.

Supply chain and logistics – are the backbone of any business. A supply chain network has scores of variables, and if one changes, it affects the entire network. There can be innumerable permutations and combinations of ways to travel from point A to point B. Businesses may struggle to find the most efficient one without the help of an ML engineer. With optimization algorithm models, ML engineers can consider all factors in a supply chain network while optimizing the most efficient path. Businesses benefit greatly from this optimization by reducing costs while improving lead time.

Machine learning engineers help businesses enhance their products and services and optimize operations. They are integral to an organization’s plans on both local and international levels.

What are the skills and qualifications for an ML Engineer?

The following skills are necessary for becoming a successful ML engineer:

  • Math and statistics form the foundation of machine learning. A person skilled at these would be able to decipher problems mathematically. Stating a situation in mathematical terms uses descriptive statistics, and testing hypotheses use inferential statistics. Together they help us solve problems with the accuracy of math.
  • Programming languages are tools that help ML engineers harness the power of computers. We need programming languages to communicate with and direct computers to perform a task. Python and R programming are the most common languages used in machine learning. Other languages to master are SQL, Java, etc., as they complement the process of creating solutions through machine learning.
  • Business acumen is a skill that is acquired with experience. Understanding business processes is vital to creating a replica of them in mathematical terms. Factors for success or failure can be quantitative and must be recognized for them to be included in a machine-learning model.
  • Successful ML engineers have a knack for problem-solving. The skill helps you look at things objectively and weigh consequences in critical decisions.
  • With technical skills, an ML engineer should also be equipped with the soft skills of communication. The role requires them to discuss and understand the exact requirements of a project and, later, effectively communicate the results and suggestions to the clients or stakeholders.

An individual can carry the skills mentioned above and become a successful ML engineer without a formal degree. They can follow a traditional path as well, which consists of the following:

  • A bachelor’s degree with a specific focus on computer science and information technology will educate you about the basics of programming languages and technological platforms.
  • A master’s degree in computer science with a specialization in data science provides a curriculum focused on applying technical and theoretical knowledge. At the same time, educational institutions offer a forum for the exchange of ideas and peer-to-peer learning around the topic of machine learning.
  • Tools and languages required to create machine-learning solutions can be quickly learned. Numerous organizations offer data science course in different aspects of machine learning. For someone who wants to upskill, certifications can be a versatile way of learning skills as they can be completed simultaneously while working on jobs.

What are the reasons for becoming an ML Engineer?

The role of a machine-learning engineer offers lucrative pay packages. Internationally, an ML engineer with deep learning skills can expect an annual compensation of nearly USD 150,000. Almost all firms require the services of an ML engineer. Hence, ML engineers enjoy a wider choice of work locations around the globe.

This vertical offers the best work-life balance in an organizational setup. All the tools required for this job are available on the cloud. An ML engineer can access them from anywhere around the globe, allowing the comfort of working in a hybrid setup.

Their work reveals a lot about an organization’s financial and operational performance. Hence, such professionals work closely with the top management at any firm. They experience exposure to all verticals and functions, making them a viable option for leading the organization if the opportunity comes.

Machine learning has applications in and is not limited to dominant industries like financial services, life sciences, IT services, consulting services, supply chains, logistics, etc. With a wide array of industries to choose from, machine learning engineers can comfortably shift from one organization to another, keeping future prospects in mind.

Sustainable automation has become vital to every organization’s vision in the urgency of climate change. A pact to save humanity has motivated organizations around the world to transform. This movement has generated a massive requirement for professionals who can track and introduce change in these companies. The global machine-learning market is expected to grow from USD 21 billion in 2022 to USD 209 billion in 2029. With a massive 38% CAGR, machine learning is among the most lucrative fields of work in the near future.


In conclusion, machine learning engineers are objective individuals who can use proper research and methodologies to develop or program self-learning data models. They build data models to address business issues that produce descriptive, predictive, and prescriptive outcomes. They manipulate data with a solid understanding of computer languages and business processes.

They gather raw data from reliable sources, which may take any format (numbers, words, images, audio, video, etc.). They remove errors, sort data, randomly generate missing data, or eliminate it. They train and test models while improving accuracy through an iterative process.

Machine learning engineers are vital to the smooth operation of diverse business functions. They help generate insight into the business’s financial performance and provide leaders with vital information to make critical decisions. They eliminate bottlenecks in business processes to make operations flawless. ML engineers generate insights into factors that motivate sales at microscopic levels and help firms to optimize funding campaigns with a positive outlook. They consider all elements in a supply chain network to optimize the most efficient path and make a profit for businesses significantly by optimizing costs and improving lead time.

Math and statistics form the foundation of machine learning, while programming languages are tools that help ML engineers harness the power of computers. Successful ML engineers have a knack for problem-solving as it helps them look at things objectively and weigh consequences in critical decisions. They are required to discuss and understand the exact requirement of a project and effectively communicate the results and suggestions to the clients or stakeholders.

An individual can carry the abovementioned skills and become a successful machine learning engineer without a formal degree. Numerous organizations offer data science and machine learning courses in different aspects of machine learning. Certifications can be a versatile way of learning skills for someone who wants to upskill while working on jobs.

Engineers that specialize in machine learning might earn substantial salaries. Also, a wider variety of job locations are available to ML engineers worldwide. In an organizational structure, this role delivers the finest work-life balance. The cloud contains every tool needed for this job, and an ML engineer can access them anywhere. They are exposed to many industries and job responsibilities, which qualifies them to take the helm of the company should the occasion arise. A wide range of relevant sectors are options to rely on for machine learning engineers.

With the fate of the earth in their minds, machine learning engineers help businesses design products and services and optimize operations. They are integral to a sustainable future on both local and international levels. And so, machine learning engineering as a career is a prosperous choice; at the same time, a very achievable one for professionals from diverse backgrounds.