🤖 AI Summary
This study addresses collaboration and communication (CoCo) challenges faced by machine learning engineering teams in the hardware-dominated semiconductor industry, where ambiguous roles, stringent data governance, protracted development cycles, and tight coupling with physical processes significantly impede system deployment, reproducibility, and maintenance. Focusing for the first time on this hardware-intensive domain, the research draws on semi-structured interviews with twelve practitioners from globally leading semiconductor firms, combined with thematic analysis and an interdisciplinary collaboration framework, to systematically identify sixteen recurrent CoCo challenge categories—among which role and responsibility ambiguity emerges as the most critical. The work further distills multiple empirically validated mitigation strategies, offering an evidence-based foundation and targeted recommendations for optimizing ML engineering collaboration tools and workflows in semiconductor manufacturing contexts.
📝 Abstract
The integration of machine learning (ML) into complex software systems has increased challenges in collaboration and communication (CoCo) of the teams building these systems. ML engineering (MLE) teams often involve diverse roles, ML engineers, data scientists, software engineers, and domain experts, each bringing unique goals, experiences, and jargon. These interdisciplinary dynamics can make it challenging to deploy, reproduce, and maintain ML-enabled systems over the long term. Previous studies have uncovered several CoCo challenges and practices, but most have focused on software-centric companies, leaving limited empirical understanding of how these dynamics unfold in hardware-centric contexts. In hardware-centric environments, CoCo challenges are shaped by additional constraints such as strict data governance, long development cycles, and tight coupling with physical processes, which amplify coordination complexity and reduce flexibility. To strengthen empirical understanding in such settings, we present a qualitative investigation of MLE teams within a global semiconductor company, where ML-enabled systems and manufacturing processes introduce additional complexity. We interviewed 12 practitioners regarding CoCo practices, tools, challenges, and approaches. Through analysis, we identified 16 recurring challenges, with unclear roles and responsibilities emerging as the most critical, and common practices and recommendations practitioners considered effective in mitigating CoCo problems. While grounded in a single organizational context, our findings align with known issues in interdisciplinary ML-enabled systems development, but also demonstrate how these challenges manifest differently under hardware-driven constraints. Our results highlight directions for future research and tool support to strengthen CoCo in MLE projects and ensure the success of ML-enabled systems.