What is an employable level of ML knowledge? /u/Kalekuda CSCQ protests reddit

What is the level of AI/ML knowledge necessary for getting a job in the field? What level of position would the following milestones of ML experience qualify someone for?

1. Model trainer (Has read the pytorch documentation and trained a local model on Mnist)

2. Model creator (Can write a backprop nueral network from scratch)

3. Novel Applicator (Has created a project involving a novel application of ML/AI using a publicly available model/API)

4. Self-starter Novel Applicator (Created a project involving a novel application of ML/AI using a custom model)

5. Unpublished Researcher (someone whose academically/professionally researched and devloped ML/AI but went unpublished. I.e. 2 YoE as a self driving car machine vision guy working to advance and apply the tech in the real world, but whose work was never published, patented or peer-reviewed)

6. Published Researcher (Grad student whose capstone was published and peer-reviewed.)

Which level of experience / “familiarity” would qualify someone enough for them to be a desirable candidate for AI/ML positions at the “entry level” and junior levels? What would a Senior AI/ML engineer’s qualifications even look like? Its such a relatively new field so even the most experienced people don’t have 10+ YoE, so I imagine that the recruitment process considers projects and practical experience as a matter of necessity and I’d like to know how highly valued the various milestones of AI/ML knowledge are. Does this differ between NN/LLM/MV roles? (Which has the highest and lowest “barrier to entry” for the mid-level roles?)

What is the level of AI/ML knowledge necessary for getting a job in the field? What level of position would the following milestones of ML experience qualify someone for?

1. Model trainer (Has read the pytorch documentation and trained a local model on Mnist)

2. Model creator (Can write a backprop nueral network from scratch)

3. Novel Applicator (Has created a project involving a novel application of ML/AI using a publicly available model/API)

4. Self-starter Novel Applicator (Created a project involving a novel application of ML/AI using a custom model)

5. Unpublished Researcher (someone whose academically/professionally researched and devloped ML/AI but went unpublished. I.e. 2 YoE as a self driving car machine vision guy working to advance and apply the tech in the real world, but whose work was never published, patented or peer-reviewed)

6. Published Researcher (Grad student whose capstone was published and peer-reviewed.)

Which level of experience / “familiarity” would qualify someone enough for them to be a desirable candidate for AI/ML positions at the “entry level” and junior levels? What would a Senior AI/ML engineer’s qualifications even look like? Its such a relatively new field so even the most experienced people don’t have 10+ YoE, so I imagine that the recruitment process considers projects and practical experience as a matter of necessity and I’d like to know how highly valued the various milestones of AI/ML knowledge are. Does this differ between NN/LLM/MV roles? (Which has the highest and lowest “barrier to entry” for the mid-level roles?)