DevRel & Technical Content Intern

DevRel & Technical Content Intern at RunAnywhere (YC W26)

RunAnywhere is a Y Combinator W26 company building the infrastructure for on-device AI. Our open-source SDK (10k+ GitHub stars, Apache-2.0) lets developers run LLMs, vision, and speech models directly on phones: iOS, Android, Flutter, React Native, and Web, with one API. QHexRT, our inference engine for the Qualcomm Hexagon NPU, is the first runtime to run the full stack (LLM, vision, speech recognition, and text-to-speech) on the NPU. No cloud, no latency, no per-token bills.

Developers are our entire go-to-market. We grow by being genuinely useful in public: reproducible benchmarks, honest technical writing, and demos that make people say "wait, that runs on a phone?"

THE ROLE

You'll be the voice of RunAnywhere in the developer community. Your job is to make developers discover, understand, and get excited about running AI on-device through writing, demos, and daily presence where developers hang out.

It's a content-first role, but not content-only: you'll actually use our SDKs to build the things you write about. You'll be "customer zero": the first developer to hit every rough edge, and the one who tells us where it hurts.

WHAT YOU'LL DO

• Write technical blogs and tutorials: integration guides, benchmark write-ups, "how on-device inference works" explainers, and deep-ish dives on things like quantization, NPU vs GPU vs CPU tradeoffs, and time-to-first-token.

• Build simple demo apps and sample projects with RunAnywhere SDKs (offline chat, on-device voice, and similar) and turn them into demos, walkthroughs, and quickstarts developers can copy.

• Create multimedia content: short demo videos, screen recordings of models running on real phones, 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. Publish consistently, grounded in real builds.

• Help improve developer onboarding: flag confusing docs and examples, suggest fixes, and remove the setup friction you hit yourself.

• Support launches: work with the founders to turn features into stories (latency, offline reliability, benchmarks) 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.

WHO WE'RE LOOKING FOR

Must have:

• A working understanding of on-device / local AI inference. You don't need to write kernels, but you should be able to explain what an inference engine does, why quantization matters, what GGUF is, and why an NPU beats a CPU on prefill, and hold your own in a technical conversation about it.

• Strong written English. You can turn a technical topic into a post a developer actually finishes reading.

• You can make things: demo videos, screen recordings, diagrams, simple sample apps. You don't wait for a designer.

• Enough coding ability to build a simple demo app and follow a mobile setup guide. Some exposure to Swift, Kotlin, React Native, or Flutter helps a lot.

• Proof you can communicate: content, demos, write-ups, or a GitHub profile we can look at.

• Already active (or eager to be) on X/Twitter and LinkedIn, and comfortable engaging strangers in technical threads.

• You've played with local models yourself using inference engines like llama.cpp, or by running models directly on your own phone.

• Comfortable moving fast as "customer zero": you try things first, break them, and tell us where it hurts.

Nice to have:

• Mobile dev experience (Android/Kotlin, iOS/Swift, Flutter, or React Native).

• Deeper on-device / AI exposure (LLMs, STT/TTS pipelines, benchmarking).

• You've written technical blogs before, or run a technical X/LinkedIn/YouTube presence with real engagement.

• Video editing skills (CapCut, Premiere, DaVinci, whatever works).

• Prior DevRel, developer community, or open-source experience.

• Familiarity with the local-AI community (r/LocalLLaMA, Hugging Face, HN).

COMPENSATION & LOGISTICS

• $750–$1,500/month, based on experience and output.

• Remote, flexible hours. Overlap with the team's working hours expected.

• Remote. Applicants from anywhere in the world are welcome.