About Liquid AI

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.

The Opportunity

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 builder role focused on shipping, not strategy: you will ship often, adapt quickly, and take on more ownership as you go.

What We're Looking For

We need someone who is a:

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 enjoy answering 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 have real opinions.
Honest technical communicator: You benchmark fairly, you do not oversell, and you have a low tolerance for vague or generated-sounding content.
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.

The Work

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 the 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. Represent Liquid at the conferences and developer events that matter for small-model and edge AI — submitting talks, running booths, and demoing in the hallway track. 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 that demonstrate LFMs on real edge hardware (iOS, Android, browser, Jetson, NPUs). Maintain integration recipes for the inference stacks developers use (llama.cpp, MLX, ONNX, executorch, LEAP SDK). Write cookbooks that take a developer from base model to fine-tuned, quantized, deployed in one sitting.
Create content that earns trust. Deeply technical blog posts, demos, and honest benchmarks against Qwen, Gemma, Phi, and SmolLM. First-principles, concrete, no LLM slop. Turn the best in-person workshop material into evergreen content so the in-room audience compounds online.
Close the loop. Maintain a friction log of developer pain points — chat templates, tokenizers, deployment gotchas — and bring the signal into roadmap conversations with the model and platform teams.

Desired Experience

Must-have:

Proven technical expertise: hands-on experience with LLMs, including model fine-tuning (LoRA, QLoRA, full fine-tuning, distillation), tokenizer debugging, and a track record of shipping to production or active community environments
Fluency with the modern AI stack: deep familiarity with PyTorch, Hugging Face (Transformers, PEFT, Datasets), and model serving frameworks (llama.cpp, MLX, vLLM, ONNX Runtime, or TGI), alongside an understanding of quantization tradeoffs (GGUF, AWQ, GPTQ, INT8/INT4)
Efficient model specialization: experience with on-device deployment (iOS, Android, embedded) or specialized work within the efficient-model ecosystem (Phi, Gemma, Qwen, SmolLM, or distilled architectures)

Nice-to-have:

Prior developer relations, developer advocacy, or community engineering experience
A track record of public technical communication: conference talks, widely read technical writing, or a following among ML and edge developers
Active open-source contributions, especially to the inference or efficient-model tooling ecosystem (llama.cpp, MLX, ONNX, Hugging Face libraries)
Experience organizing or running hackathons, workshops, or developer events

What Success Looks Like (Year One)

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, 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 the developer community shows up for, with measurable community growth as a result.
A working developer-friction feedback loop: pain points are surfaced and fixed, OSS contributions land, talks get accepted, and inbound developer interest grows.

What We Offer

Build the function: You are defining how Liquid goes to market technically, with direct influence on product direction and access to the founding team.

Member of Technical Staff - Developer Relations