Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry

TL;DR

This study investigates collaboration and communication challenges in ML engineering teams within the semiconductor industry, highlighting unique issues and practices in hardware-centric contexts through qualitative interviews.

Contribution

It provides the first empirical insights into CoCo challenges specific to hardware-centric ML engineering teams, identifying key issues and mitigation strategies.

Findings

Identified 16 recurring CoCo challenges, with unclear roles being most critical.

Practitioners use specific practices and tools to mitigate CoCo problems.

Hardware constraints amplify coordination complexity in ML teams.

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.