Days, job title Data Scientist,,,,, Machine Learning Engineerand AI Engineer Everywhere – If you are like me, if you are not working on the spot, it is difficult to understand what each of them actually does.
Then there are titles that sound more confusing – Quantum Blockchain LLM Robot Engineer (Okay, I made one, but you get it).
The job market is full of buzzwords and overlapping roles, and if you are interested in a career in machine learning, it’s hard to know where to start.
In this article, I will break down top machine learning roles and explain what each role involves – and the work required to prepare them.
Data Scientist
What’s this?
Data scientists are the most famous role, but have the greatest job responsibilities.
Generally, there are two types of data scientists:
- Analysis and experiment centered.
- The focus of machine learning and modeling.
The former involves running A/B tests, performing deep dives to identify where the business can improve, and improving machine learning models by identifying blind spots. Many of the works in this work are called interpretive data analysis or EDA for short.
The latter is mainly to build POC machine learning models and decision-making systems that benefit from the business. Then, using software and machine learning engineers to work together to deploy these models to production and monitor their performance.
Many machine learning algorithms are often on the simpler side and perform regular supervised and unsupervised learning models, such as:
- xgboost
- Linear and logistic regression
- Random Forest
- K-mean clustering
I’m a data scientist at my old company, but I mostly built machine learning models and didn’t do many A/B tests or experiments. That’s the work done by data analysts and product analysts.
However, in my current company, data scientists do not build machine learning models, but do mainly engage in deep participation in analyses and measure experiments. Model development is mainly done by machine learning engineers.
It really depends on the company. Therefore, it is very important to read the job description to make sure this is the right job for you.
What do they use?
As a data scientist, these are usually things you need to know (this is not exhaustive and will vary by character):
- Python and SQL
- git and github
- Command line (bash and zsh)
- Statistics and Mathematics Knowledge
- Basic machine learning skills
- Point Cloud System (AWS, Azure, GCP)
I have a roadmap on becoming a data scientist, and you can see below if this character is interested.
Machine Learning Engineer
What’s this?
The title shows that machine learning engineers are building machine learning models and deploying them into production systems.
It was originally from software engineering, but now it is its own work/title.
The significant difference between machine learning engineers and data scientists is that machine learning engineers deploy algorithms.
As Chip Huyen, a leading AI/ML practitioner, said:
The purpose of data science is Generate business insightsand the goal of ML engineering is Turn data into products.
You will find that data scientists usually come from a strong background in mathematics, statistics, or economics, and machine learning engineers come from a more scientific and engineering background.
However, this role overlap is a big overlap, and some companies may bundle positions as data scientists and machine learning engineers into individual jobs, often bundling them together with data scientist titles.
The job of machine learning engineers is often found in more mature tech companies; however, it is gradually becoming more popular over time.
Further specialties exist in the role of machine learning engineers, such as:
- ML Platform Engineer
- ML Hardware Engineer
- ML Solutions Architect
If you are a beginner, don’t worry about these, as they are quite niche and only make sense after the experience in the field has been a few years. I just wanted to add these so you know the various options out there.
What do they use?
For machine learning engineers, the technology stack is very similar to data scientists, but has more software engineering elements:
- However, Python and SQL may require other languages. For example, in my current role, it takes rust.
- git and github
- bash and zsh
- AWS, Azure, or GCP
- Software engineering basics such as CI/CD, MLOPS and Docker.
- Excellent machine learning knowledge, ideally a field of expertise.
AI Engineer
What’s this?
This is a new title, and all the AI hype is currently in progress, and honestly, I think it’s a weird title, not really needed. Typically, machine learning engineers will play the role of AI engineers in most companies.
Most AI engineers’ roles are actually about Genai, not the entire AI. This distinction usually makes no sense to people outside the industry.
However, AI includes almost any decision algorithm and is larger than the field of machine learning.
The current definition of AI engineers is the person who helps the business primarily with LLM and Genai tools.
They don’t necessarily develop basic algorithms from scratch, mainly because it’s hard to do unless you’re in a research lab, and many of the top models are open source, so you don’t have to reinvent the wheel.
Instead, they focus on tweaking and building the product first, and then worrying about fine-tuning the model later. So, they
It is close to traditional software engineering compared to the current role of machine learning engineers. Although many machine learning engineers will run as AI engineers, the work is new and not fully fulfilled.
What do they use?
This role is developing a lot, but overall you need knowledge of all the latest Genai and LLM trends:
- Solid-Software Engineering Skills
- Python, SQL, and backend Langauges (such as Java or Go) are useful
- CI/CD
- git
- LLM and transformers
- rag
- Timely engineering
- Basic Model
- Fine adjustment
I also recommend you check out Datacamp’s Associates AI Engineer’s Data Scientist track, which will also give you a career as a data scientist. This is linked in the description below.
Research scientist/engineer
What’s this?
Previous roles were primarily industry positions, but the next two will be based on research.
The industry role is mainly related to business and it all involves generating business value. Whether you are using linear regression or transformer models, what matters is the impact, not necessarily the method.
The research aims to expand current knowledge capabilities theoretically and in practice. This approach revolves around deep experiments in scientific methods and niche fields.
The difference between research and industry is vague and often overlaps. For example, many of the top research labs are actually large tech companies:
- Meta-research
- Google AI
- Microsoft AI
These companies initially started solving business problems, but now have specialized research areas so you can solve industry and research problems. One starts, the other end is not always clear.
If you are interested in exploring the differences between research and industry more deeply, I recommend reading this document. This is the first speech at Stanford University CS 329S, Lecture 1: Understanding Machine Learning Production.
Often, there are more jobs in the industry than research, because only large companies can afford data and computational costs.
Anyway, as a research engineer or scientist, you will essentially be committed to cutting-edge research, thus pushing the boundaries of machine learning knowledge.
There is a slight difference in the work between the two. As a research scientist, you will need a PhD, but for a research engineer, that is not necessarily true.
Research engineers usually implement the theoretical details and ideas of research scientists. This role is usually a full-fledged research company; in most cases, the work of research engineers and scientists is the same.
The company may offer the title of research scientist because it gives you more “influence” and makes you more likely to do the job.
What do they use?
This is similar to machine learning engineering, but the depth of knowledge and qualifications is usually greater.
- Python and SQL
- git and github
- bash and zsh
- AWS, Azure, or GCP
- Software engineering basics such as CI/CD, MLOPS and Docker.
- Excellent machine learning knowledge and expertise in cutting-edge fields such as computer vision, enhanced learning, LLM, and more.
- PhD or at least a master’s degree in the relevant discipline.
- Research experience.
This article just scratches the surface of machine learning characters, and there are more niche jobs and professions in these four or five I mentioned.
I always recommend you step onto the door and turn to the direction you want to go to start your career. This strategy is much more effective than a tunnel vision with only one character.
Another thing!
I offer a 1:1 coaching call where we can chat with anything you need – whether it’s a project, career advice or just figuring out your next step. I’m here to help you move forward!
1:1 Guidance phone number with Egor Howell
Career guidance, job advice, project assistance, restoration reviewtopmate.io
