About the role

About RunAnywhere

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?”

Founders

You’ll work directly with our founders. Sanchit Monga (CEO) previously built mobile SDKs at Intuit used across 50M+ devices. Shubham Malhotra (CTO) built MetalRT, the fastest LLM inference engine on Apple Silicon, and previously worked on EC2 Spot at Amazon and Azure at Microsoft. We started RunAnywhere because shipping on-device AI was painful and no existing tool solved it end to end.

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.

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 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.

Your first 30/60/90 days

• First 30 days: set up the RunAnywhere SDK on your own device, run our benchmarks, and ship your first blog post comparing NPU vs CPU performance.
• First 60 days: publish 2-3 more technical posts or demos, and build a regular presence in developer communities like r/LocalLLaMA, Hacker News, and Discord.
• First 90 days: own a piece of our content calendar end-to-end, with a visible, recognized presence across X/Twitter, LinkedIn, and technical forums.

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.
• 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.

Nice to have:

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