Member of Technical Staff – Developer Relations

Liquid AI

Acting as the technical voice for LFM developers in community engagement. Building and shipping reference applications for edge AI across deployment targets and communities.

Posted 5/22/2026full-timeSan Francisco • California • 🇺🇸 United StatesLeadWebsite

Tech Stack

Tools & technologies

AndroidC++iOSPyTorch

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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

Benefits

Comp & perks

We pay 100% of medical, dental, and vision premiums for employees and dependents
401(k) matching up to 4% of base pay
Unlimited PTO plus company-wide Refill Days throughout the year

ATS Keywords

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Hard Skills & Tools

LLMsmodel fine-tuningLoRAQLoRAdistillationtokenizer debuggingPyTorchHugging Facemodel serving frameworksquantization tradeoffs

Soft Skills

public technical communicationdeveloper relationsdeveloper advocacycommunity engineering