Developer Relations

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 developer relations role focused on building, writing, and engaging publicly. You will ship reference applications and technical resources, communicate regularly with developers across editorial and community channels, and help establish a credible, consistent developer voice for Liquid.

Sitting within Marketing & Communications, you will work closely with our model, platform, and product teams. Technical teams will partner on accuracy, while you will own how the work is explained, presented, and adapted for developer audiences.

We need someone who is a:

• 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)

• Demonstrated experience in developer relations, developer advocacy, or community engineering, including direct responsibility for engaging and supporting developers publicly.

• A strong portfolio of public technical communication, such as technical articles, tutorials, cookbooks, talks, videos, workshops, or open-source educational content, that demonstrates accuracy, clarity, judgment, and an authentic voice.

• Strong technical writing and editing skills, with the ability to turn complex model, tooling, and deployment concepts into content that developers can understand and act on.

• 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

• 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.