The distinction between an "AI engineer" and an "AI researcher"

ChatGPT, OpenAI.

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The distinction between an "AI engineer" and an "AI researcher" often lies in the nature of the work and the environment, although the boundaries can sometimes be blurred:

1. AI Engineer:
   • Typically works in the industry, applying established AI and machine learning techniques to real-world problems.
   • Might develop and implement machine learning models, optimize existing algorithms for specific applications, integrate AI capabilities into software applications, etc.
   • The focus is often on practical application, scalability, and deployment of AI models.
2. AI Researcher:
   • Often found in academia, research labs, or the research departments of tech companies.
   • Works on advancing the field of AI by developing new theories, algorithms, or techniques.
   • The focus is on innovation, exploration, and expanding the understanding of AI, which can be later applied by AI engineers.
   • Writing and publishing research papers is a common activity for AI researchers.

Considering you're aiming to complete a Ph.D. in AI, your immediate role during that period would likely align more with an "AI researcher" since Ph.D. studies are typically research-oriented. However, that doesn't mean you can't take on engineering tasks or projects during your Ph.D.; many doctoral candidates do.

Once you finish your Ph.D., you can choose to continue in research (becoming a Postdoc, then perhaps a Professor or a Research Scientist in a tech company) or transition more into the engineering side, applying the knowledge you gained during your research.

In summary, your title or role would depend on the nature of your work at any given time. During your Ph.D., you'd likely be more of an "AI researcher," but post-Ph.D., you could be either an AI engineer or continue as a researcher, depending on your career choices and the nature of your job.