Member of Technical Staff
Liquid AI
Member of Technical Staff
Liquid AI
Provides generative, edge-capable foundation models
Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.
Developers do not yet grasp what a fine-tuned small model can do until they see one running on a phone, in a browser, or on a Jetson board. Closing that imagination gap is the job. You will be Liquid's technical voice where builders already are (Hugging Face, GitHub, Discord, and the developer events that matter for edge AI), turning individual troubleshooting sessions on chat templates, quantization, and on-device deployment into cookbooks, reference apps, and content the whole community can use.
This is a hands-on role within Marketing & Communications. You will work closely with our model, platform, and product teams: they partner on technical accuracy, and you own how the work is explained, presented, and adapted for developers.
We need someone who:
Builder: You would rather ship a working reference app than write a strategy memo, and you measure yourself by what developers actually adopt.
Community native: You answer hard questions in public, you are comfortable being wrong in front of people, and you know the small-model and edge-AI ecosystem well enough to hold real opinions.
Trusted technical voice: You move between a quick Discord answer, a step-by-step cookbook, and a long-form technical post without losing accuracy or voice, and you earn the trust of skeptical developers by staying concrete and avoiding hype.
In-person energizer: Hackathons, workshops, hallway-track demos, and the conferences where builders actually show up are where you do your best work.
End-to-end owner: You take ownership in ambiguity and turn one-off problems into reusable resources without being asked.
Be the technical voice in the community: live where LFM developers already are (Hugging Face, GitHub, Discord, X), answer hard questions in public, maintain Liquid's presence on the Hub, and amplify community fine-tunes and edge deployments worth seeing.
Show up in person: host hackathons, build nights, and technical workshops at Liquid offices and partner venues; submit talks, run booths, and demo at the conferences that matter for small-model and edge AI; and partner with hackathon organizers to make LFMs the obvious choice for builders who care about latency, on-device, or cost.
Build the on-device and adaptation story: ship reference applications on real edge hardware (iOS, Android, browser, Jetson, NPUs), maintain integration recipes for llama.cpp, MLX, ONNX, ExecuTorch, and write cookbooks that take a developer from base model to fine-tuned, quantized, and deployed in one sitting.
Create content that earns trust: deeply technical blog posts, demos, and honest benchmarks against Qwen, Gemma, Phi, and SmolLM, and turn the best workshop material into evergreen content so the in-room audience compounds online.
Close the loop: keep a friction log of developer pain points (chat templates, tokenizers, deployment gotchas) and bring that signal into roadmap conversations with the model and platform teams.
What you'll need:
Hands-on LLM experience, including fine-tuning (LoRA, QLoRA, full fine-tuning, distillation) and tokenizer debugging, with a track record of shipping to production or active community environments.
Fluency with PyTorch, Hugging Face (Transformers, PEFT, Datasets), and at least one model serving framework (llama.cpp, MLX, vLLM, ONNX Runtime, or TGI), plus a working understanding of quantization tradeoffs (GGUF, AWQ, GPTQ, INT8/INT4).
Experience with on-device deployment (iOS, Android, embedded) or specialized work in the efficient-model ecosystem (Phi, Gemma, Qwen, SmolLM, or distilled architectures).
Experience in developer relations, developer advocacy, or community engineering, with direct responsibility for engaging and supporting developers publicly.
A public portfolio of technical communication (articles, tutorials, cookbooks, talks, videos, workshops, or open-source educational content) that turns complex model, tooling, and deployment concepts into material developers can act on.
Helpful, not required:
Open-source contributions to the inference or efficient-model tooling ecosystem (llama.cpp, MLX, ONNX, Hugging Face libraries).
Experience organizing or running hackathons, workshops, or developer events.
The LFM quickstart on Hugging Face is the cleanest path to production in the small-model ecosystem, with measurable lift in fine-tunes, downloads, and downstream Spaces.
A library of on-device reference apps, with at least one each on iOS, Android, and an embedded target, that external developers fork, extend, and ship.
A regular cadence of in-person events (LFM hackathons, edge-AI build nights, technical workshops) that developers show up for, with measurable community growth as a result.
A working developer-friction feedback loop: pain points get surfaced and fixed, OSS contributions land, talks get accepted, and inbound developer interest grows.
Build the function: You define how Liquid goes to market technically, with direct influence on product direction and access to the founding team.
Compensation & Equity: Competitive base salary plus equity, so you share in what we build. Final offers reflect skills, experience, and location.
Time Off: Unlimited PTO, plus company-wide Refill Days, long weekends when the whole company rests together.
Health & Wellbeing: Comprehensive health coverage, with details varying by location.
Paid Leave: Paid leave for pregnancy, parenting, medical needs, and caring for family.
Liquid AI builds and deploys foundation models based on liquid neural networks, focusing on efficient, on-device AI. It develops Liquid Foundation Models (LFMs), a family of generative AI models designed to be smaller and more computation-efficient than typical large language models, enabling deployment on edge devices with lower latency, better privacy, and reduced infrastructure costs. The approach includes end-to-end AI expertise and customizable architectures for enterprises that require real-time performance and private processing. Compared to traditional AI providers, Liquid AI emphasizes edge-ready, adaptable models that run efficiently on constrained hardware, and it targets enterprise-grade, private, reliable AI solutions. The company’s goal is to enable real-time, on-device AI at scale for businesses by offering compact, capable foundation models and the tools to customize them for specific applications.
Explore more opportunities at Liquid AI