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Developer Relations

Liquid AI· Marketing
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Hybrid (San Francisco)Full-timePosted 1w ago

About the role

Developer Relations role focused on building, writing, and engaging publicly to establish Liquid AI's developer voice and resources for edge AI and small-model deployments.

Responsibilities

  • 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 conferences and developer events that matter for small-model and edge AI — submitting talks, running booths, and demoing in hallway tracks. Partner with hackathon organizers to promote LFMs for latency, on-device, or cost benefits.
  • Build the on-device and adaptation story. Ship reference applications demonstrating LFMs on edge hardware (iOS, Android, browser, Jetson, NPUs). Maintain integration recipes for inference stacks (llama.cpp, MLX, ONNX, executorch, LEAP SDK). Write cookbooks guiding base model to fine-tuned, quantized deployments in one sitting.
  • Create content that earns trust. Produce deeply technical blog posts, demos, and benchmarks against Qwen, Gemma, Phi, SmolLM with first-principles, concrete content.
  • Close the loop. Maintain a friction log of developer pain points and bring signals into roadmap conversations with model and platform teams.

Qualifications

  • Proven technical expertise: hands-on experience with LLMs, including model fine-tuning (LoRA, QLoRA, full fine-tuning, distillation), tokenizer debugging, and production/community shipping.
  • Fluency with the modern AI stack: PyTorch, Hugging Face (Transformers, PEFT, Datasets), and model serving frameworks (llama.cpp, MLX, vLLM, ONNX Runtime, TGI); understanding of quantization tradeoffs (GGUF, AWQ, GPTQ, INT8/INT4).
  • Efficient model specialization: on-device deployment (iOS, Android, embedded) or experience in efficient-model ecosystems (Phi, Gemma, Qwen, SmolLM, or distilled architectures).
  • Demonstrated experience in developer relations, advocacy, or community engineering with public developer engagement.
  • Strong portfolio of public technical communication (articles, tutorials, cookbooks, talks, videos, workshops, open-source content).
  • Strong technical writing/editing skills translating concepts into developer-ready content.

Skills

Required:LLM model fine-tuningTokenizer debuggingPyTorchHugging Face ecosystemOn-device deployment

Benefits

  • Competitive base salary with equity in a unicorn-stage company
  • Health: 100% 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
Last updated Oct 14, 2026. Interested? Apply on the company site.
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About Liquid AI

Build efficient general-purpose AI at every scale.

We build efficient general-purpose AI at every scale. Liquid Foundation Models (LFMs) are a new generation of generative AI models that achieve state-of-the-art performance at every scale, while maintaining a smaller memory footprint and more efficient inference.

AIEdge AIFoundation ModelsGenerative AIliquid-ai-recruiting-target
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