Open-Source Machine Learning Engineer
New
Flexible working arrangements across Europe, including the United KingdomFull-Time
Salary not disclosed
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Job Details
- Languages
- English
- Required Skills
- PythonMachine LearningPyTorchTensorflowGitHubDeep Learning
Requirements
- Strong proficiency in Python with a focus on writing clean, maintainable, and production-quality library code.
- Solid hands-on experience with deep learning frameworks, particularly PyTorch (JAX or TensorFlow also considered).
- Familiarity with modern machine learning concepts, including transformer architectures and large-scale model training.
- Demonstrated experience contributing to open-source projects with visible contributions on GitHub.
- Experience working with or within the Hugging Face ecosystem or similar ML libraries is a strong advantage.
- Ability to collaborate effectively in open-source environments, including code reviews, issue tracking, and community support.
- Strong understanding of distributed collaboration workflows and asynchronous communication practices.
- Excellent written English skills for technical documentation and global collaboration.
Responsibilities
- Contribute to the development, improvement, and maintenance of major open-source machine learning libraries and frameworks.
- Design and implement high-quality, well-tested, and maintainable Python-based library code used by the global ML community.
- Collaborate with contributors and users through GitHub issues, pull requests, forums, and community discussions.
- Improve deep learning frameworks and tooling, particularly around transformer models, training workflows, and inference optimization.
- Support and enhance ecosystem libraries such as PyTorch-based tooling and related ML infrastructure components.
- Participate in technical discussions to define roadmap priorities and shape the evolution of open-source projects.
- Help debug, review, and improve community contributions while maintaining high code and documentation standards.
- Work on performance improvements, scalability, and usability enhancements for large-scale ML systems.
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