🤖 AI Summary
This work addresses the widespread absence of structured career pathways for Research Software Engineers (RSEs) in higher education institutions, which has hindered effective recognition of their technical expertise, scholarly contributions, and leadership potential. The project introduces the first standardized yet flexible RSE career ladder within a university setting, spanning levels from Assistant to Principal and offering dual-track progression in both technical and managerial roles. By integrating a competency framework, external expert consultation, standardized job grading, and market-aligned salary benchmarking, the framework achieves a balanced alignment between institutional human resources policies and individual professional development needs. Implementation of this system has demonstrably enhanced recruitment efficiency, increased transparency in promotion processes, and garnered strong endorsement from the RSE community.
📝 Abstract
Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.