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Liquid AI, spun out of MIT CSAIL, builds general-purpose AI systems optimized for efficient deployment across data centers and edge hardware. The company partners with enterprises in consumer electronics, automotive, life sciences, and financial services.
You will serve as Liquid's technical voice in the developer community, closing the imagination gap between what small, fine-tuned models can do in theory and what developers see running on phones, browsers, and embedded devices. You will operate where builders already congregate—Hugging Face, GitHub, Discord, and developer events focused on edge AI—turning individual troubleshooting conversations into reusable cookbooks, reference applications, and community resources.
Reporting into Marketing & Communications, you will work closely with model, platform, and product teams. Your responsibilities include:
- Maintaining Liquid's technical presence across community platforms (Hugging Face Hub, GitHub, Discord, X), answering hard questions publicly, and amplifying community fine-tunes and edge deployments.
- Representing Liquid at hackathons, workshops, and developer conferences that matter for small-model and edge AI, including submitting talks, running booths, and conducting hallway-track demos.
- Shipping reference applications demonstrating LFMs on real edge hardware (iOS, Android, browser, Jetson, NPUs) and maintaining integration recipes for inference stacks developers use (llama.cpp, MLX, ONNX, executorch, LEAP SDK).
- Creating deeply technical, honest content—blog posts, benchmarks, and tutorials—that earns the trust of skeptical technical audiences. Turning workshop material into evergreen content.
- Maintaining a friction log of developer pain points and surfacing signal into roadmap conversations with technical teams.
Success in year one means the LFM quickstart on Hugging Face becomes the cleanest path to production in the small-model ecosystem; a library of on-device reference apps across iOS, Android, and embedded targets that external developers fork and extend; a regular cadence of in-person events with measurable community growth; and a working developer-feedback loop that surfaces pain points, lands OSS contributions, and drives inbound developer interest.
You will define how Liquid goes to market technically, with direct influence on product direction and access to the founding team.
REQUIREMENTS:
Must-have:
- Proven technical expertise with LLMs, including hands-on experience with model fine-tuning (LoRA, QLoRA, full fine-tuning, distillation), tokenizer debugging, and a track record of shipping to production or active community environments.
- Deep fluency with the modern AI stack: PyTorch, Hugging Face (Transformers, PEFT, Datasets), and model serving frameworks (llama.cpp, MLX, vLLM, ONNX Runtime, TGI), with understanding of quantization tradeoffs (GGUF, AWQ, GPTQ, INT8/INT4).
- Experience with on-device deployment (iOS, Android, embedded) or specialized work within the efficient-model ecosystem (Phi, Gemma, Qwen, SmolLM, distilled architectures).
- Demonstrated experience in developer relations, developer advocacy, or community engineering, with direct responsibility for engaging and supporting developers publicly.
- Strong portfolio of public technical communication (technical articles, tutorials, cookbooks, talks, videos, workshops, open-source educational content) demonstrating accuracy, clarity, judgment, and authentic voice.
- Strong technical writing and editing skills, able to translate complex model, tooling, and deployment concepts into actionable developer content.
Nice-to-have:
- Active open-source contributions to inference or efficient-model tooling (llama.cpp, MLX, ONNX, Hugging Face libraries).
- Experience organizing or running hackathons, workshops, or developer events.
