What Do Machine Learning Engineers Do?

In this article, I want to explain precisely what I do as a machine learning engineer. 

The aim is to help anyone looking to enter the field gain a truthful view of what a machine learning engineer is, how we work, what we do, and what a typical day in life is like. 

I hope it can help you pinpoint if a career in machine learning is indeed for you.

What is a machine learning engineer?

Due to the rapid acceleration of the tech/AI space, a machine learning engineer is still not well-defined and varies between companies and geographies to a certain extent.

However, it generally refers to someone who:

Mixes machine learning, statistics and software engineering skills to train and deploy models into production.

At some companies, there will be a large cross-over with data scientists. Still, the main distinction between the two roles is that machine learning engineers deliver the solution into production. Often, data scientists won’t do this and focus more on helping in the model-building stage.

The need for a machine learning engineer came from the fact that models in Jupyter Notebooks have zero value. So, a role well-versed in machine learning and software engineering was needed to help bring the models “to life” and ensure they generate business value.

Because of this broad skillset, machine learning engineering is not an entry-level role, and you would typically need to be a data scientist or software engineer for a couple of years first.

So, to summarise:

• Responsibilities: Train, build and deploy machine learning models.
• Skills & Tech: Python, SQL, AWS, Bash/Zsh, PyTorch, Docker, Kubernetes, MLOps, Git, distributed computing (not an exhaustive list).
• Experience: A couple of years as a data scientist or software engineer, and then up-skill yourself in the other areas.

If you want a better understanding of the different data and machine learning roles, I recommend checking out some of my previous articles.

The Difference Between ML Engineers and Data Scientists
Helping you decide whether you want to be a data scientist or machine learning engineermedium.com

Should You Become A Data Scientist, Data Analyst Or Data Engineer?
Explaining the differences and requirements between the various data rolesmedium.com

What do I do?

I work as a machine learning engineer within a cross-functional team. My squad specialises in classical machine learning and combinatorial optimisation-based problems.

Much of my work revolves around improving our machine learning models and optimisation solutions to improve the customer experience and generate financial value for the business.

The general workflow for most of my projects is as follows:

• Idea — Someone may have an idea or hypothesis about how to improve one of our models.
• Data — We check if the data to prove or disprove this hypothesis is readily available so we can start the research.
• Research — If the data is available, we start building or testing this new hypothesis in the model.
• Analysis — The results of the research stage are analysed to determine if we have improved the model.
• Ship — The improvement is “productionised” in the codebase and goes live.

Along this process, there is a lot of interaction with other functions and roles within the team and broader company.

• The idea phase is a collaborative discussion with a product manager who can provide business insight and any critical impacts we may have missed in the initial scoping.
• Data, Build, and Analysis can be done in collaboration with data analysts and engineers to ensure the quality of our ETL pipelines and the use of the right data sources.
• The research section would use the help of data scientists to use statistics and machine learning skills when looking to improve our model.
• The ship phase is a joint effort with our dedicated software engineers, ensuring our deployment is robust and up to standard with best coding practices.

From experience, I know that this type of workflow is prevalent among machine learning engineers in numerous companies, although I am sure there are slight variations depending on where you are.

My job is also not just to write code day in and day out. I have other responsibilities, like conducting workshops, presenting to stakeholders, and mentoring more junior members.

What is the structure of machine learning teams?

Machine learning engineers work in many different ways across an organisation, but there are three distinct options, and the rest are a mix of them.

• Embedded — In this case, machine learning engineers are embedded in cross-functional teams with analysts, product managers, software engineers and data scientists, where the team solves problems in one domain within the company. This is how I work, and I really like it because you get to pick up lots of valuable skills and abilities from other team members who are specialists in their own right.
• Consultancy — This is the flip side, where machine learning engineers are part of an “in-house consultancy” and are part of their own team. In this scenario, the machine learning engineers work on problems based on their perceived value to the business. You are technically less specialised in this option as you may need to change the type of problems you work on.
• Infrastructure/Platform — Instead of solving business problems directly, these machine learning engineers develop in-house tools and a deployment platform to make productionising the algorithms much easier.

All ways of working have pros and cons, and in reality, I wouldn’t say one is better than the other; it’s really a matter of personal preference. You still do exciting work, nonetheless!

What is a typical day in a life?

People online often glamourise working in tech, like it’s all coffee breaks, chats, and coding for an hour a day, and you make well over six figures.

This is definitely not the case, and I wish it was true, but it’s still a fun and enjoyable workday compared to many other professions.

My general experience has been:

• 9:00 am — 9:30 am. Start at 9 am with a morning standup to catch up with the team regarding the previous day’s work and what you are doing today. A “standup” meeting is very common across tech.
• 9:30 am — 10:30 am. After the standup, there may be another meeting for an hour, 9:30–10:30 or so, with stakeholders, engineers, an all-hands or other company meetings.
• 10:30 am — 13:00 pm. Then, it’s a work/code block for two hours or so where I focus on my projects. Depending on my work, I may pair with another data scientist, machine learning engineer or software engineer.
• 13:00 pm — 14:00 pm. Lunch.
• 14:00 pm — 17:45 pm. Afternoons are normally free of meetings, and there is a large block of focus time to work on your projects. This is mainly for individual contributors like myself.
• 17:45 pm — 18:00 pm. Reply to emails and Slack messages and wrap up for the day.

Every day is different, but this is what you can expect. As you can tell, it’s nothing “extrordinary.”

This is also the workday for a junior / mid-level individual contributor (IC) like myself. Senior positions, especially managerial roles, typically have more meetings.

An important thing to note is that I don’t always code in my work blocks. I may have a presentation to prepare for stakeholders, some ad-hoc analysis for our product manager, or some writing up of my latest research. I may not even code for the whole day!

On average, I spend 3–4 hours hard coding; the rest is meetings or ad-hoc work. Of course, this varies between companies and at different times of the year.

Why am I’m a machine learning engineer?

The reason I am a machine learning engineer can be boiled down to four main reasons:

• Interesting. As a machine learning engineer, I get to be correct at the forefront of the latest tech trends like AI, LLMs, and pretty much anything that is going viral in the field. There is always something new and exciting to learn, which I love! So, if you want to constantly learn new skills and apply them, this may be a career you would be interested in.
• Work-Life Balance. Tech jobs generally provide better work-life balance than other professions like banking, law or consulting. Most machine learning jobs are 9–6, and you can often spend a few days working from home. This flexibility allows me to pursue other passions, projects, and hobbies outside of work, such as this blog!
• Compensation. It’s no secret that tech jobs provide some of the highest salaries. According to levelsfyi, the median salary of a machine learning engineer in the UK is £93k, which is crazy for an average value.
• Range of Industries. As a machine learning engineer, you can work in loads of different industries during your career. However, to become a real specialist, you must find and stick to one industry you love.

I hope this article gave you more insight into machine learning, if you have any questions let me know in the comments.

Another thing!

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