Developer Relations & Technical Content Intern
Remote$750/mo - $1.5k/moInternPosted 3mo ago
About the role
Intern role focused on developer relations and technical content for RunAnywhere, a YC-funded company building on-device AI infrastructure. Write blogs, create demos, engage in technical communities, and support product launches.
Responsibilities
- Write technical blogs and tutorials: integration guides, benchmark write-ups, 'how on-device inference works' explainers, and deep-dives on quantization, NPU vs GPU vs CPU tradeoffs, and time-to-first-token.
- Build demos and multimedia content: short demo videos, screen recordings of models running on real phones, sample apps, and visual content for launches.
- Engage the community everywhere: X/Twitter, LinkedIn, Reddit (r/LocalLLaMA and friends), Hacker News, Discord, and GitHub. Answer questions, join conversations about local/on-device AI, and rep the project without being spammy.
- Support launches: help draft and distribute content for model releases, benchmark drops, and feature announcements.
- Turn community signal into content: the questions developers keep asking become the next blog post, demo, or docs page.
Qualifications
- A working understanding of on-device / local AI inference.
- Strong written English.
- Ability to create demo videos, screen recordings, diagrams, and simple sample apps.
- Active (or eager) on X/Twitter and LinkedIn, comfortable engaging with strangers in technical threads.
- Experience with local models or inference engines like llama.cpp or running models on a phone.
Skills
Required:On-device AI inferenceTechnical writingContent creation (video/visuals)
Benefits
- $750-$1,500/month, based on experience and output.
- Remote, flexible hours. Overlap with the team’s working hours expected.
Last updated Oct 25, 2026. Interested? Apply on the company site.
About RunAnywhere
The default way of running on-device AI at Scale
Edge AI is inevitable, but shipping it is painful: every device class behaves differently, runtimes vary, models are huge, and performance collapses under memory/power constraints. RunAnywhere turns that into an enterprise-ready workflow: one SDK to run models on-device, plus a control plane to manage models, enforce policies, and measure outcomes across thousands of devices.