A Day in the Life of a Machine Learning Engineer

If you are passionate about machine learning and wish to learn all about it, you are not alone! Machine learning is one such concept that is drawing the attention of more and more people each day. But while it is one thing to be interested in machine learning, it is a completely different thing to work in the field of machine learning. In this article, my aim is to help you understand who a machine learning engineer is and how his typical day would be like.

In order to master any concept, it is important to start with the basics.

What is Machine Learning?

Machine learning, a subset of artificial intelligence, refers to machines being able to learn by themselves without being explicitly programmed. It is an application of AI which enables systems to learn and improve from experience automatically. While working with machine learning, various sets of algorithms are required. These algorithms use a set of training data to enable computers to learn.

Who is a Machine Learning Engineer?

The primary goal of a machine learning engineer is artificial intelligence. A machine learning engineer is, in the most basic sense, a computer programmer, but the difference lies in focus. Machine learning focus beyond programming machines to perform specific tasks. The typical example of a system a machine learning engineer would work on is a self-driving car. They are involved in creating programs that will help machines take actions without being directed specifically to perform those tasks. Right from customized news feeds to tailored web searches, machine learning engineers impact the daily life of many individuals and the way they use this technology.

Basic Qualifications Needed

The field of study of a machine learning engineer must be mathematics or computer science. A majority of the employers who hire machine learning engineers will expect candidates to have a master’s or doctoral degree in a relevant discipline. He must be well-versed in computer programming, and knowledge of specific programming languages such as Java or C++ will be an added advantage.

Basic Skills Needed

For a machine learning career, the following skills are needed.

  • Exceptional mathematical skills to perform computations.


  • In-depth knowledge of working with algorithms.


  • Communication skills to explain machine learning processes to people from non-technical backgrounds.
  • Basic writing skills, as some positions may require machine learning engineers to publish articles on their work.

  • Strong analytical skills for projecting outcomes and isolating issues that must be solved to make programs more effective.


Skills Employers Look For in Machine Learning Engineers

The main skills employers across the globe look for in machine learning engineers are:

  • Fundamentals of computer science and programming– These include knowing about data structures (trees, graphs, stacks, queues, multi-dimensional arrays), computer architecture (memory, bandwidth, distributed processing, cache), and algorithms (sorting, searching, optimization).




  • Probability and statistics– One must be familiar with probability concepts such as Bayes rule, Bayes Nets, Markov decision processes, etc. Statistics concepts a machine learning engineer must know are mean, median, variance, hypothesis testing, and normal, uniform, and binomial distributions.




  • Data modeling and evaluation– A machine learning engineer must be able to estimate the underlying structure of a dataset to find useful patterns. A key part of this process is evaluating how good a given model is.




  • Machine learning algorithms and libraries– One needs to be familiar with choosing suitable models such as neural net, decision tree, an ensemble of multiple models, linear regression, genetic algorithms, boosting, and bagging. A machine learning engineer must also know the pros and cons of various approaches such as missing data, data leakage, bias and variance, and overfitting and underfitting.




  • Software engineering and system design– Bottomline, a machine learning engineer’s deliverable or typical output, is software. He needs to design the system carefully to let algorithms scale well and avoid bottlenecks.




A Typical Day of a Machine Learning Engineer

So, how does a typical day of a machine learning engineer look like, and what are the various roles he needs to fulfill?

Let us assume that a machine learning engineer starts his day at 9 a.m. From 9 a.m.-10 a.m., he catches up on the projects and code that has been in operation through the night. He then checks his work emails and the to-do-list for the day.

From 10 a.m.-12 p.m., he gives importance to work calls. This is the time when he gets cracking with machine learning tools and projects. He codes or designs a database or learns new concepts with the help of resourceful tools such as H20, Scikit Learn, etc.

Post lunch, say at 1 p.m., he takes care of office meetings and client calls when he discusses the progress of the proposed ideas for new projects and products and ongoing projects.

From 2 p.m.-5 p.m., he tests the completed models, writes unit tests, and completes the ongoing tasks. After completing these tasks, he will check the metrics of the existing models and compare these to the model baseline. He then continues with coding and also reviews the requests from the client’s side.

Between 6 p.m. and 8 p.m., he wraps up everything, such as database models, code requests, and projects. He makes sure nothing is pending.

Even after heading home, he will check his inbox maybe by 10 p.m., to check if there are any work-related issues and in case of any, he solves the ones demanding immediate action.

Let us now list down some of the responsibilities of a machine learning engineer.

  • Analyzes machine learning algorithms to solve a given problem.
  • Exploring and visualizing data.
  • Identifying differences in the data distribution.
  • Verifying data quality and ensuring data quality through data cleaning.
  • Supervising the data acquisition process.
  • Defining validation strategies.
  • Understanding business objectives and developing models.
  • Managing resources, such as hardware, data, and personnel.


It is important for a machine learning engineer to understand the entire ecosystem for which he is designing. Perhaps the limitless applicability of machine learning is what makes it more compelling. The great news for machine learning enthusiasts is that there are virtually no fields to which machine learning cannot be applied. Many fields, such as finance, manufacturing, education, and information technology, are greatly impacted by machine learning. The world is facing complex challenges as it is changing rapidly and dramatically, and these challenges can only be solved using complex systems. Machine learning engineers are the ones who design these systems. If you are very sure that machine learning is your cup of tea, then there is no time like the present to develop the mindset and master the skills needed to succeed in your machine learning career.

To know more about machine learning certifications and become a machine learning expert, check out Global Tech Council.