To be a machine learning engineer again, this is the exact process I will follow.
Let’s get started!
Become a data scientist or software engineer first
I’ve said it before, but machine learning engineers are not an entry-level position.
This is because you need skills in many areas:
- Statistics
- math
- Machine Learning
- Software Engineering
- DevOps
- Cloud system
You certainly don’t need to be all the experts, but you should have reliable knowledge.
Today’s machine learning engineers can be highly paid technical jobs. According to LevelFyi, the average salary in the UK is:
- Machine Learning Engineer: £93,796
- AI researchers: £83,114
- AI Engineer: £75,379
- Data Scientist: £71,005
- Software Engineer: £83,168
- Data Engineer: £69,475
LevelSfyi is usually at the top end, as the companies on its website are often large tech companies that usually pay higher salaries.
With all this in mind, this is not to say that you can’t get a job as a machine learning engineer from a university or university right away; it’s just very rare that I barely see.
If you have the right background, such as a master’s or doctorate in CS with AI/ML or a math, you are more likely to get a general machine learning role, but not a required machine learning engineering.
So for most people, I recommend you first become a data scientist or software engineer or a software engineer for a few years and then hopefully become a machine learning engineer.
This is exactly what I did.
I’ve been a data scientist for 3.5 years and then transitioned to a machine learning engineer, and this path is common among machine learning engineers in my current company.
Whether you are a data scientist or a software engineer, it depends on you and your background and skills.
So determine which role is best for you and try to find a job in the field.
There are many roadmaps for software engineers and data scientists on the internet; I believe you can easily find one that suits your learning style.
I have some data science that you can check below.
If I start learning data science from 2025, I’ll do
How will I make my data science learning more effective
How do I become a data scientist (if I have to start over)
Roadmap and tips on how to find a job in data science
Working on a machine learning project
Once you have obtained work as a data scientist or software engineer, your goal should be to develop and work on a production machine learning project.
If a machine learning department or project exists in your current company, the best way to do this is to deal with it.
For example, one of my friends, Arman Khondker, runs the newsletter “AI Engineer”, I highly recommend you check it out, from a software engineer at Tiktok to working at Microsoft AI.
According to his newsletter:
In tiktok, I’ve been working Tiktok StoreI and Algorithm Team– Includes ML Engineers and Data Scientists (for your page) Recommended Engines.
This experience ultimately helped me Transition to full-time AI at Microsoft.
But, for me, it’s the opposite.
As a data scientist, you want to work with machine learning engineers and software engineers to understand how things can be deployed into production.
In my previous company, I was a data scientist who was developing machine learning algorithms, but not independently shipped them to production.
So I asked if I could work on a project where I could study the model and end with very little engineering support.
It’s hard, but I’ve learned a lot. Eventually, I started to ship the solution to production with ease.
Even though my title is a data scientist, I became a machine learning engineer by nature.
My advice is to talk to your manager, express your interest in developing machine learning knowledge, and ask if you can work on some of these projects.
In most cases, your manager and company will adapt even if it takes several months to assign you to a project.
Even better, if you can move to a team focused on machine learning products, such as the advice on Tiktok Shop, then this will speed up your learning as you will be constantly discussing machine learning topics.
Skills of opposite skills
This has something to do with the previous point, but as I said before, machine learning engineers need a broad range of knowledge, so you need to improve your skills in your weaker areas.
If you are a data scientist, you may be weak in engineering fields such as cloud systems, DevOps, and writing production code.
If you are a software engineer, you may be weak in math, statistics, and machine learning knowledge.
You want to find areas that need improvement and focus on.
As we discussed earlier, the best way is to connect it to your daily routine, but if this is impossible, or if you want to speed up your knowledge, then you will need to learn in your spare time.
I know some people may not be that way, but if you want to work on top of the top paying technical jobs, you will need to work extra on the job!
I do this by writing blogs about software engineering concepts, researching data structures and algorithms, and improve writing of my production code in my spare time.
Developing machine learning major
One thing that really helped me was developing a professional in machine learning.
I am a data scientist specializing in time series prediction and optimization problems, and I have served as a machine learning engineer specializing in optimization and classical machine learning.
One of the main reasons I got the role of a machine learning engineer is that I have a deeper understanding of optimization than the average machine learning person. That’s my edge.
The role of a machine learning engineer is often aligned with an expert, so a very good understanding of one or several areas will greatly improve your chances.
As far as Oman is concerned, he knows very well the recommendation systems and how to deploy them at scale; he even said this himself in his newsletter:
This work gave me first-hand experience:
– Large scale Recommended system
– AI-powered Ranking and personalization
– End to end ML deployment pipeline
So I recommend working in a team that focuses on a specific field of machine learning, but honestly, this is usually the case in most companies, so you don’t need to think too much about it.
If you can’t work on a company’s machine learning project, you need to learn hours away again. I always recommend that you learn the basics first, but then really think about the areas where you want to explore and learn Deeepr.
Here is a detailed list of machine learning experts for some inspiration:
- Natural Language Processing (NLP) and LLM
- Computer Vision
- Reinforcement learning
- Time series analysis and prediction
- Exception detection
- Recommended system
- Voice recognition and processing
- optimization
- Quantitative analysis
- Deep Learning
- Bioinformatics
- Econometrics
- Geospatial analysis
I usually recommend you learn about 2 to 3 in a nice depth, but if you want to transition quickly, narrow it down to one. However, check if there is enough demand for this skill set.
After becoming a machine learning engineer, you can develop more specialties over time.
I also recommend that you check out an article on how to specialize in machine learning.
How to specialize in data science/machine learning
Is it better to be a generalist or expert?
Start running as a machine learning engineer
In tech companies, it is often said that to get a promotion, you should run at the above level for 3-6 months.
The same is true if you want to become a machine learning engineer.
If you are a data scientist or software engineer, you should try your best to become a machine learning engineer at your current company.
Who knows, they can even change your title and give you the job of a machine learning engineer in your current workplace! (I heard this happened.)
What I really got to is the identity switch. You want to think and be like a machine learning engineer.
This mindset will help you learn more and better structure machine learning interviews.
You will have a range of confidence and a range of proven projects that make an impact.
You can always say, “I’m basically a machine learning engineer at my current company.”
I did this and the rest was history, as they said.
Another thing!
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