Machine learning engineer Knowledge areas

Hi, AI community,

I was thinking about a top-tier machine learning engineer knowledge spectrum, Do we have people working in Computer vision with knowledge of object detection/YOLO/face recognition/image segmentation and NLP with a focus on sequence models/ transformers/LLM/text knowledgebase bots /traditional NLP.
What is it like can a person work on everything, or does he/she specialize in either NLP engineering or computer Vision? I know if maybe there is a multi-modal AI architecture project information of both worlds will come in handy but are there designated teams?


But there are also individual contributors.

It all depends on who is organizing the work, and the scope of projects they are working on.

There’s no specific answer to this question.

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A bit late but could you please explain the general responsibilities of mle?
In terms of

  1. Data Pipelines - xx%
  2. Model development - xx%
  3. Research on new models or making new models from literature - xx%
  4. MLops - xx%

When i mean research i mean say i have paper 1,2,3 and i use the knowlege to model llm -1,2 with 3. just example

Your question is overly broad. Depending on the job, any one of those percentages can range from 0 to 100%.

I would say Data pipeline and MLops are quite linked together. But again like what @TMosh mentioned, it depend on the company you working with. Eg, if you are in a newly formed Startup, you basically will end up doing every single areas. In a big enterprise like Google, FB etc, you may end up doing a very small subset, eg creating a small part of the MLops pipeline etc. Hope that help a bit .