Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Deepak Babu Piskala

TL;DR

The paper addresses the blurring boundary between research and engineering in AI organizations amid rapid scaling of large models. It employs a qualitative synthesis of public job postings and org narratives to propose the AI Roles Continuum, a spectrum spanning RS, RE, AS, and MLE. It provides a competency taxonomy and a role-skill heatmap to map overlaps, and discusses implications for hiring, career ladders, and workforce development. The findings suggest that embracing fluid roles accelerates iteration from research to production, enhances organizational learning, and necessitates cross-functional collaboration and shared tooling, while acknowledging limitations of public-artifact data up to 2025.

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.

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

TL;DR

The paper addresses the blurring boundary between research and engineering in AI organizations amid rapid scaling of large models. It employs a qualitative synthesis of public job postings and org narratives to propose the AI Roles Continuum, a spectrum spanning RS, RE, AS, and MLE. It provides a competency taxonomy and a role-skill heatmap to map overlaps, and discusses implications for hiring, career ladders, and workforce development. The findings suggest that embracing fluid roles accelerates iteration from research to production, enhances organizational learning, and necessitates cross-functional collaboration and shared tooling, while acknowledging limitations of public-artifact data up to 2025.

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.
Paper Structure (7 sections, 2 figures)

This paper contains 7 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Hybrid Roles in Leading AI Organizations
  3. Shared Technical Skills and Responsibilities Across Roles
  4. Role Definitions and Skill Requirements in Practice
  5. Scaling AI Infrastructure and Cross-Functional Team Structures
  6. Organizational Demand Along the Research--Engineering Continuum
  7. Conclusion

Figures (2)

  • Figure 2: Role × Skill involvement heatmap. Darker cells indicate higher typical involvement by a role in that skill (none→strong). The visualization emphasizes overlaps and complementary strengths across Research Scientists (RS), Research Engineers (RE), Applied Scientists (AS), and ML Engineers (MLE), making shared competencies and role-specific focus areas apparent at a glance.
  • Figure 4: Illustrative hiring distribution along the research--engineering continuum by organization type. Frontier labs peak on the research-leaning side with fewer total openings; enterprises (vertical AI / SaaS / big tech) peak on the engineering-leaning side with more openings.