The AI Roles Continuum: Blurring the Boundary Between Research and Engineering

TL;DR

The paper introduces the AI Roles Continuum, a framework showing how AI research and engineering roles overlap, promoting fluidity to enhance organizational learning and efficiency in AI development.

Contribution

It proposes a new framework that blurs traditional role boundaries in AI organizations, supported by qualitative analysis of organizational practices and a competency taxonomy.

Findings

Roles are overlapping rather than discrete.

Shared core competencies across roles.

Fluid roles improve research-to-production cycles.

Abstract

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.