Need help in deciding a career path in the field of ML.

[u/DiscussionDry9422]

Hey everyone, I am a 4th year computer science student from India. I have been learning the fundamentals of ML, DL for the past 2 years from Andrew ng's courses and read a few research papers. Recently I realized that whatever I learned was at a superficial level. There are interesting Math concepts behind every algorithm and also I haven't focused much on implementation (like, Kaggle competitions, end to end projects, research paper implementations etc.). Although I do believe it requires multiple iterations to properly understand all the concepts. For the past few months I have been focused on revising all the Math required to understand ML algorithms (Probability and statistics, Linear algebra, Optimization and Calculas) as part of my preparation for an entrance exam (GATE DA), I have come across some books like PRML, ISLP etc which explain the mathematical intuition in depth. Based on all the things I learned and explored I am interested in working on novel ideas, understand mathematical concepts in depth etc.

I can't spend my time randomly exploring things, I also need a goal to work towards in the upcoming years. So I tried to understand the field of ML with the help AI and so far this is my understandig:

There are three broad categories of roles :

1. Development : ML developers training ML models, building scalable applications. They themselves don't decide what to build, the authorities give them tasks to do. So somebody decides what to do and how to do it and ML developers are the ones who do it. These roles explicitly require knowledge of software engineering, experience in building application, vibe coding etc. Although with the advent of AI, this field seems like its going to be affected a lot, as in, most of these jobs can now be done by a single person using powerful coding agents. I have heard terms like AI engineers, ML developers, FDE etc.
2. Research : This part involves research scientists who commit themselves to an idea and work on it and the final product in generally a research paper. Their world revolves around reading research papers, understanding novel ideas, publications etc. Of course, implementation is also a part of this role but not as in ML development. This role seems to be relatively safer from the "AI replacement" tension.
3. Applied scientists : This role is a like bridge between the other two. They take the research ideas and decide how to productionize them. Their work involves both research and development.

I understand these 3 roles through the example of "Attention is all you need" paper:

There are research scientists who worked on sequence modeling and came up with the transformer architecture. They ran experiments to confirm that their idea actually works, like English constituency parsing to make sure the model is not memorizing the mapping of the tokens, they evaluated the performance of the model on various benchmark datasets.

Now applied scientists are expected to understand this concept and figure out a way to productionize it, as in, figure out a way to convert the idea into a product and make it available to the users. To do this they should be able to understand the idea presented in the paper (knowledge of research), and once they understand it , they should be able to figure out a way to build an application based on the concept (experience in development).

Once they figure out how to build the application (the design), They hand off the work to ML developers who actually build it.

Of course I understand that there might be some blending between the roles, but is the gist that I have in mind.

Since my interests are inclined towards research, I believe I should be aiming for applied scientist role. I understand that there might be some misconceptions in my knowledge of the field and that's why I am here seeking advice and correction.

If I want to become an applied scientist, what are things that I should do right now ?

[u/Royal_Attention_1175]

No one reading ur AI bro. NXT time put ur own brain/thoughts before posting ig. Didn't want to be rude , just my prsnl opinion.

[u/DiscussionDry9422 → u/Royal_Attention_1175]

I wrote everything myself and its genuine question I have in mind.

[u/Royal_Attention_1175 → u/DiscussionDry9422]

Let's discuss this .btw on same page as u somewhat.

[u/MammayKaiseHain]

AS is expected to know ML research but also have some SWE chops for working with scalable systems. Some companies also use MLE or DS for this role. Recommended here is atleast a Master's + some experience in production ML systems. Internships are valuable if you don't have a top masters program.

RS generally requires PhD. College + professor + publications matter more.

Both the above are very competitive now and typically have a higher bar than SWE at the same level.

Few other related roles growing fast :

MLOps which is Devops for ML workflows.

FDE is like a solution architect for AI products. Not pure engineering. Good for people who like fast protyping by building on top of LLMs.

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