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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.06087 |
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| _version_ | 1866912813823295488 |
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| author | Piskala, Deepak Babu |
| author_facet | Piskala, Deepak Babu |
| contents | The rapid scaling of deep neural networks and large language models has collapsed the once-clear divide between "research" and "engineering" in AI organizations. Drawing on a qualitative synthesis of public job descriptions, hiring criteria, and organizational narratives from leading AI labs and technology companies, we propose the AI Roles Continuum: a framework in which Research Scientists, Research Engineers, Applied Scientists, and Machine Learning Engineers occupy overlapping positions rather than discrete categories. We show that core competencies such as distributed systems design, large-scale training and optimization, rigorous experimentation, and publication-minded inquiry are now broadly shared across titles. Treating roles as fluid rather than siloed shortens research-to-production loops, improves iteration velocity, and strengthens organizational learning. We present a taxonomy of competencies mapped to common roles and discuss implications for hiring practices, career ladders, and workforce development in modern AI enterprises. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06087 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The AI Roles Continuum: Blurring the Boundary Between Research and Engineering Piskala, Deepak Babu Computers and Society The rapid scaling of deep neural networks and large language models has collapsed the once-clear divide between "research" and "engineering" in AI organizations. Drawing on a qualitative synthesis of public job descriptions, hiring criteria, and organizational narratives from leading AI labs and technology companies, we propose the AI Roles Continuum: a framework in which Research Scientists, Research Engineers, Applied Scientists, and Machine Learning Engineers occupy overlapping positions rather than discrete categories. We show that core competencies such as distributed systems design, large-scale training and optimization, rigorous experimentation, and publication-minded inquiry are now broadly shared across titles. Treating roles as fluid rather than siloed shortens research-to-production loops, improves iteration velocity, and strengthens organizational learning. We present a taxonomy of competencies mapped to common roles and discuss implications for hiring practices, career ladders, and workforce development in modern AI enterprises. |
| title | The AI Roles Continuum: Blurring the Boundary Between Research and Engineering |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2601.06087 |