How to apply for Resonance Technical Fellow

About Resonance

Resonance works on a different vision for AI, where communities have a say in how it is built, governed, and used. The role is part-time, remote, and six months long, with significant ownership and technical mentorship. It is a fit for someone who wants to build with real communities from the start rather than ship features in isolation.

About the role

You will build a simple web application that helps local hosts organize Resonance gatherings, share agendas and resources, and let participants engage. You will work directly with hosts and communities, iterate based on use, and release the work as open source. You will also help design Resonance's first community-owned AI experiments using open-weight models and locally governed data or knowledge.

A typical day

A typical day might include a call with a Resonance host to review how the gatherings tool is being used, then coding and shipping an update based on that feedback. You might also spend time testing an open-weight model on local hardware and writing notes on what a community could realistically run and maintain. The work is remote, part-time, and self-directed, with regular check-ins for technical mentorship.

Who Resonance is looking for

• Can build and ship a lightweight web application end to end, including frontend, backend, and deployment.
• Has worked directly with non-technical users or community groups and can iterate based on feedback.
• Understands open-weight models and can assess what can realistically run locally for a rural library, nonprofit, or local government.
• Writes clear documentation and is comfortable releasing work as open source for others to adapt.
• Can manage a part-time, remote fellowship with significant ownership and limited supervision.

Tips for this application

• Show a past project where you built a tool for real users, not just a demo, and explain how you changed it after watching people use it.
• Name specific open-weight models or local hosting setups you have tried, and what ran well or poorly on modest hardware.
• Explain how you would keep community data or knowledge under local governance, not just where it would be stored.
• Point to open source work you have released and documented so other communities could reuse it.
• State your availability clearly for a six-month part-time remote fellowship and how you will work with hosts across time zones.

What to cover in your cover letter

['A concrete example of building a lightweight tool with and for a community, including what you learned from users.', 'Your hands-on experience with open-weight models and local or self-hosted deployment, with specifics on hardware and tradeoffs.', 'How you think about community ownership of AI in practice, including data governance and who maintains control.', 'Why a six-month part-time fellowship with significant ownership fits your current situation and how you will manage the scope.']

Research before applying

• Find out what Resonance gatherings are and how local hosts currently organize them, so you can describe the first project accurately.
• Look for any public writing or talks from Resonance about community-owned AI and their vision for AI governance.
• Check what open-weight models and local hosting tools are currently practical for small organizations, including hardware and maintenance needs.
• Ask what technical mentorship looks like in this fellowship and who you would work with day to day.

Likely interview topics

Based on the job description, expect questions about:

• Walk through a web application you built for non-technical users and how you iterated after seeing it used.
• Which open-weight models would you consider for a rural library, nonprofit, or local government, and what could realistically run locally?
• When does self-hosting matter for a community, and when is it not worth it?
• How would you design a community-owned AI experiment where the community maintains meaningful control over data or knowledge?
• How would you document and release a tool so another community can understand, adapt, and build on it?

Common mistakes to avoid

• Proposing a complex platform or heavy AI stack instead of the lightweight tools the role asks for.
• Talking about AI in the abstract without naming open-weight models, local hosting, or data governance specifics.
• Ignoring the community side of the work and focusing only on code, when the role requires working directly with hosts and participants.