About the Role
This role involves collaborating with researchers and developers to advance machine learning applications in non-AI scientific areas, contributing to open-source tools and community-driven projects.
Responsibilities
- Collaborate with scientists to integrate machine learning into non-AI research domains
- Develop and maintain open-source software tools for scientific machine learning
- Support community-led research projects through technical guidance
- Contribute to documentation and tutorials for broader accessibility
- Engage with academic and research communities across EMEA
- Identify challenges in applying ML to scientific workflows
- Prototype tools that bridge domain-specific research and ML methods
- Facilitate knowledge sharing between technical and non-technical collaborators
- Organize workshops and community events on ML in science
- Improve tooling for reproducibility in scientific computing
- Advocate for open science practices within research communities
- Monitor emerging trends in scientific computing and ML integration
- Provide feedback on usability of ML frameworks in real research settings
- Assist in adapting models for domain-specific data constraints
- Promote best practices in version control and collaborative coding
- Troubleshoot technical issues in community-contributed code
- Help researchers deploy lightweight ML solutions locally
- Encourage modular design in scientific software development
- Support integration of ML tools with existing research pipelines
- Gather user requirements from diverse scientific disciplines
- Contribute to discussions on ethical use of ML in research
- Work with cross-disciplinary teams on shared technical challenges
- Enhance accessibility of tools for non-expert programmers
- Report community needs back to core development teams
- Assist in evaluating performance of ML models in real-world science contexts
Compensation
Competitive salary and benefits package
Work Arrangement
Remote within EMEA
Team
Part of a global team focused on open science and machine learning collaboration
Why This Role Matters
- Scientific progress increasingly depends on accessible machine learning tools tailored to domain-specific needs.
- This position directly supports researchers who lack specialized ML training but want to apply data-driven methods responsibly.
- By focusing on non-AI fields, the role helps expand the impact of open machine learning beyond traditional tech domains.
What We Value
- Practical problem-solving over theoretical perfection
- Collaboration across disciplines and cultures
- Transparency and openness in research methods
- Sustainable, maintainable code over quick fixes
- Empowering others through knowledge sharing
Not applicable for remote roles
