Open-Source Machine Learning Engineer

Accountabilities:

• 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.

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.


• Bonus: experience with distributed training, GPU optimization, inference performance, or maintaining open-source ML projects.

• Competitive compensation package with equity participation opportunities.


• Fully remote role with flexible working arrangements across Europe, including the United Kingdom.


• Opportunity to work on globally adopted open-source ML tools used by millions of practitioners.


• Strong culture of learning, research collaboration, and continuous technical development.


• Access to conferences, training, and professional development support.


• Flexible working hours and generous time-off policies supporting work-life balance.


• Collaborative, inclusive, and globally distributed engineering culture.


• Opportunity to influence the direction of major machine learning frameworks and tools.


• Work alongside leading contributors in the open-source AI and ML ecosystem.

This listing is from ats_lever. View original listing ↗