Job description

AI is changing how we solve problems with software. But some fundamentals remain. Whether the audience is an experienced developer, a newly minted builder, or a coding agent, there is often a gap between what a team assumes will work and what its users can understand, adopt, and use successfully. MLH’s Developer Intelligence Network exists to bridge that gap. Using quantitative research, such as surveys, and qualitative research, such as interviews, we give technology companies insights that help them decide what to build, how to bring it to market, and where the developer experience needs to improve. As our Developer Research Coordinator , you will help make that research possible. You will coordinate studies, manage our developer participant panel, and turn raw research material into organized, analysis-ready evidence. You’ll learn developer experience research by doing it while supporting companies ranging from startups to some of the world’s best-known technology brands. Major League Hacking’s mission is to empower hackers. Each year, more than 100,000 young technologists and innovators around the world attend MLH events and join our community. MLH recently acquired DEV (dev.to), an online community for software developers. Together, our ecosystem supports millions of creators at every stage of their journey. What you'll do: • Coordinate research studies, including participant invitations, scheduling, reminders, follow-ups, and other logistics from start to finish. • Support roughly one substantive study each month, typically involving 10–20 participants. • Keep overlapping stages of research moving, including recruiting for upcoming studies, coordinating active fieldwork, and processing recently completed research. • Clean transcripts, organize responses, tag useful quotes, and identify initial themes and patterns using established LLM-assisted workflows. • Review AI-generated outputs critically, verify themes against the source material, and flag conclusions that are inaccurate, overstated, or unsupported. • Produce an analysis-ready evidence pack for each study that the lead researcher can interpret and turn into final findings. • Run the developer panel day to day, including recruitment, onboarding, profile maintenance, participant matching, regular communication, and responding to participant questions. • Test onboarding experiences, documentation, code examples, and developer workflows, recording points of confusion or friction. • Support the quarterly MLH Census, including processing and conducting initial analysis across thousands of responses.

Requirements

Must-haves

Coordinate research studies, including participant invitations, scheduling, reminders, follow-ups, and other logistics from start to finish.
Support roughly one substantive study each month, typically involving 10–20 participants.
Keep overlapping stages of research moving, including recruiting for upcoming studies, coordinating active fieldwork, and processing recently completed research.
Clean transcripts, organize responses, tag useful quotes, and identify initial themes and patterns using established LLM-assisted workflows.
Review AI-generated outputs critically, verify themes against the source material, and flag conclusions that are inaccurate, overstated, or unsupported.
Produce an analysis-ready evidence pack for each study that the lead researcher can interpret and turn into final findings.
Run the developer panel day to day, including recruitment, onboarding, profile maintenance, participant matching, regular communication, and responding to participant questions.
Test onboarding experiences, documentation, code examples, and developer workflows, recording points of confusion or friction.
Support the quarterly MLH Census, including processing and conducting initial analysis across thousands of responses.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillm

Platform: recruitee
Effort: medium