Research has become increasingly reliant on software, serving as the driving force behind bioinformatics, high performance computing, physics, machine learning and artificial intelligence, to name a few. While substantial progress has been made in advocating for the research software engineer, a kind of software engineer that typically works directly on software and associated assets that go into research, little attention has been placed on the workforce behind research infrastructure and innovation, namely compilers and compatibility tool development, orchestration and scheduling infrastructure, developer environments, container technologies, and workflow managers. As economic incentives are moving toward different models of cloud computing and innovating is required to develop new paradigms that represent the best of both worlds, an effort called "converged computing," the need for such a role is not just ideal, but essential for the continued success of science. While scattered staff in non-traditional roles have found time to work on some facets of this space, the lack of a larger workforce and incentive to support it has led to the scientific community falling behind. In this article we will highlight the importance of this missing layer, providing examples of how a missing role of infrastructure engineer has led to inefficiencies in the interoperability, portability, and reproducibility of science. We suggest that an inability to allocate, provide resources for, and sustain individuals to work explicitly on these technologies could lead to possible futures that are sub-optimal for the continued success of our scientific communities.

翻译：研究正日益依赖于软件，软件已成为生物信息学、高性能计算、物理学、机器学习与人工智能等领域的核心驱动力。尽管在倡导研究软件工程师（这类工程师通常直接从事研究用软件及相关资产的开发）方面已取得显著进展，但研究基础设施与创新背后的劳动力——即编译器与兼容性工具开发、编排与调度基础设施、开发环境、容器技术以及工作流管理器等领域——却鲜少受到关注。随着经济激励模式正转向不同的云计算范式，并且需要创新以发展融合两者优势的新范式（即“融合计算”），设立此类角色不仅是理想选择，更是科学持续发展的必要条件。尽管分散在非传统岗位上的工作人员已抽时间涉足该领域的某些方面，但由于缺乏规模化的劳动力和相应的支持激励，科学界已处于落后状态。本文将重点阐述这一缺失层级的重要性，通过实例说明基础设施工程师角色的缺失如何导致科学成果在互操作性、可移植性与可复现性方面的效率低下。我们认为，若无法明确分配资源、支持并维持专门从事这些技术工作的人员，可能导致未来科学界的发展无法达到最优状态，从而影响科学事业的持续成功。