  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.
