With the increasing popularity of machine learning (ML), many open-source software (OSS) contributors are attracted to developing and adopting ML approaches. Comprehensive understanding of ML contributors is crucial for successful ML OSS development and maintenance. Without such knowledge, there is a risk of inefficient resource allocation and hindered collaboration in ML OSS projects. Existing research focuses on understanding the difficulties and challenges perceived by ML contributors by user surveys. There is a lack of understanding of ML contributors based on their activities tracked from software repositories. In this paper, we aim to understand ML contributors by identifying contributor profiles in ML libraries. We further study contributors' OSS engagement from three aspects: workload composition, work preferences, and technical importance. By investigating 7,640 contributors from 6 popular ML libraries (TensorFlow, PyTorch, Keras, MXNet, Theano, and ONNX), we identify four contributor profiles: Core-Afterhour, Core-Workhour, Peripheral-Afterhour, and Peripheral-Workhour. We find that: 1) project experience, authored files, collaborations, and geographical location are significant features of all profiles; 2) contributors in Core profiles exhibit significantly different OSS engagement compared to Peripheral profiles; 3) contributors' work preferences and workload compositions significantly impact project popularity; 4) long-term contributors evolve towards making fewer, constant, balanced and less technical contributions.

翻译：随着机器学习（ML）日益普及，众多开源软件（OSS）贡献者被吸引参与开发和采用ML方法。全面理解ML贡献者对于成功开发和维护ML OSS至关重要。缺乏此类认知可能导致ML OSS项目中资源分配低效与协作受阻。现有研究主要通过用户调查关注ML贡献者感知的困难与挑战，而基于软件仓库活动追踪的ML贡献者理解尚存不足。本文旨在通过识别ML库中的贡献者画像来理解ML贡献者，并进一步从工作量构成、工作偏好和技术重要性三个维度研究贡献者的OSS参与行为。通过调查来自6个流行ML库（TensorFlow、PyTorch、Keras、MXNet、Theano和ONNX）的7,640名贡献者，我们识别出四种贡献者画像：核心-业余时间型、核心-工作时间型、边缘-业余时间型、边缘-工作时间型。研究发现：1）项目经验、编写文件数、协作关系和地理位置是所有画像的显著特征；2）核心画像贡献者与边缘画像贡献者表现出显著不同的OSS参与模式；3）贡献者的工作偏好与工作量构成显著影响项目流行度；4）长期贡献者会逐渐转向更少、更稳定、更均衡且技术性更弱的贡献模式。