--- title: 'Developer Relations at Liquid AI' canonical: 'https://feeny.ai/job/developer-relations-liquid-ai-san-francisco-cxa12bxjfrdn' type: 'job' last_seen: '2026-10-13' --- # Developer Relations at Liquid AI - **Company:** Liquid AI - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-10-10 - **Last confirmed live:** 2026-10-13 - **Apply:** https://jobs.ashbyhq.com/liquid-ai/a385c0a8-1a4b-4c82-86d5-47a553d9c237 ## Job description ## 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 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. ## 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. - Full-range writer: You move fluidly between a quick answer on Discord, a step-by-step cookbook, and a long-form technical post without losing accuracy or voice. - 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. - Exceptional public technical communicator: You can turn complex technical work into clear, accurate, compelling writing, talks, demos, and tutorials. You communicate with a distinct human voice, benchmark fairly, avoid hype, and know how to earn the trust of a skeptical technical audience. 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) - 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. Nice-to-have: - 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. - Compensation: Competitive base salary with equity in a unicorn-stage company - Health: We pay 100% of medical, dental, and vision premiums for employees and dependents - Financial: 401(k) matching up to 4% of base pay - Time Off: Unlimited PTO plus company-wide Refill Days throughout the year ## About Liquid AI ## Company Overview - **One-liner**: Liquid AI builds efficiency-first, general-purpose foundation models designed to run on-device and at the edge, delivering state-of-the-art performance with minimal compute. - **Entity Type**: Private (Series A) - **Headquarters**: Cambridge, Massachusetts, United States - **Founded**: 2023 - **Founders**: Ramin Hasani (CEO), Mathias Lechner (CTO), Alexander Amini (CSO), Daniela Rus ## Core Business - **Primary Industry**: Artificial Intelligence / Foundation Models - **Target Customers**: B2B; Enterprise (e.g., automotive, finance, defense, e-commerce, biotech); developers building on-device or edge AI applications. - **Mission**: To build efficient general-purpose AI at every scale — highly capable, compute-optimized, and ready to run on any device. ## Products & Services - **Liquid Foundation Models (LFMs)**: A new generation of generative AI models optimized for on-device and edge deployment. They achieve state-of-the-art performance with a smaller memory footprint and more efficient inference than traditional transformers. Models range from 230M to 24B parameters, including specialized variants for vision (VL), multilingual search (Retrievers), and mixture-of-experts (MoE). - **Liquid Edge AI Platform (LEAP)**: A full-stack SDK for fine-tuning, baking, and deploying LFMs to production. Supports runtimes like llama.cpp, MLX, ONNX, CoreML, SGLang, and vLLM, enabling rapid customization and deployment. - **Research & White-Box Models**: The company publishes its research on liquid neural networks and state-space models in the open, emphasizing explainable, traceable AI over black-box approaches. ## Market Standing - **Valuation**: Not publicly disclosed. - **Key Metric**: - **Total Funding**: $293.2M - **Annual Revenue**: $34.5K (early-stage; likely pre-revenue or nominal) - **Notable Investors/Partners**: AMD Ventures (led Series A), OSS Capital, Stephen Pagliuca; partners include Mercedes-Benz (in-car intelligence), Insilico Medicine (drug discovery), and hardware/infrastructure providers. - **Growth Signals**: - Headcount of ~91 employees (as of mid-2026), with 8% monthly growth. - Active job postings: 18 (quarterly growth of +63.6%). - Rapid international expansion: operates in 12 countries including Japan, Germany, Spain, and France. - Recent product launch: LFM2.5 series (June 2026) — next-gen on-device models. - Strong talent pipeline: hires from MIT CSAIL, Stanford, Amazon, Meta, and Citadel. ## Competitive Advantages - **Efficiency-First Architecture**: LFMs are designed from the ground up for compute- and cost-optimized inference, enabling deployment on phones, laptops, cars, and other edge devices where traditional LLMs are too large or slow. - **White-Box Explainability**: The company prioritizes transparent, inspectable models over black-box systems — a key differentiator for regulated industries. - **Rapid Customization**: The LEAP SDK allows clients to fine-tune and deploy specialized models in minutes, not weeks. - **Strong Academic Roots**: Spun out of MIT CSAIL with a founding team of world-class researchers in liquid neural networks and state-space models. ## Strategic Focus - **On-Device & Edge AI**: Primary growth vector is bringing advanced intelligence to processors outside data centers (automotive, mobile, IoT, defense). - **Enterprise Partnerships**: Deepening collaborations with Mercedes-Benz (automotive) and Insilico Medicine (biotech) to embed LFMs into real-world products. - **International Expansion**: Building out teams in Japan, Europe, and beyond to capture global demand for localized, on-device AI. - **Model Line Expansion**: Continuously releasing new model sizes and modalities (vision, retrieval, MoE) to cover a broader range of use cases. ## Why Work Here - **Mission-Driven & High-Impact**: Work on frontier AI research and products that directly shape how intelligence is deployed in the physical world. - **Culture of Ownership**: “Leadership sets the destination, but the route is yours to discover and deliver” — high autonomy and trust, with a focus on building over process. - **Transparent & Meritocratic**: Decisions are documented and traceable; ideas are valued based on evidence, not hierarchy. - **Global & Diverse Team**: 91 employees across 12 countries; 30% in technical roles, 9% in research. Offices in Cambridge, MA (HQ) and satellite hubs in Japan and Europe. - **Perks & Environment**: - Hybrid/office policy (Cambridge HQ with multiple local offices). - Strong emphasis on employee wellbeing: “the company prioritizes the needs and wellbeing of its people.” - Continuous learning culture: “everyone is expected to continuously fine-tune their skills.” - **Recent Open Roles (as of mid-2026)**: ML Research Engineer, Applied ML (RecSys, Vision, Post-Training), Distributed Training Engineer, Solutions Architect, Founding Account Executive, Finance Manager. ## Sources 1. [Liquid AI Official Company Page](https://www.liquid.ai/company) 2. [Liquid AI Official Homepage](https://www.liquid.ai/) 3. [Liquid AI Careers Page](https://jobs.ashbyhq.com/liquid-ai) 4. [Liquid AI LinkedIn](https://www.linkedin.com/company/liquid-ai-inc) 5. [Liquid AI About Page](https://www.liquid.ai/company/about) ## Other roles at Liquid AI - [Strategic Business Development, Life Sciences](https://feeny.ai/job/strategic-business-development-life-sciences-liquid-ai-boston-abmzewrv1rne) — Boston, MA - [Controller](https://feeny.ai/job/controller-liquid-ai-san-francisco-k2rh6jk06zz6) — San Francisco, CA - [Strategic Business Development](https://feeny.ai/job/strategic-business-development-liquid-ai-san-francisco-xb9vz7j1b4v8) — San Francisco, CA - [Member of Go-to-Market Staff](https://feeny.ai/job/member-of-go-to-market-staff-liquid-ai-north-america-l-san-francisco-boston-3h9m36kbxe17) — North America L San Francisco Boston, OR - [Executive Assistant & Boston Office Manager](https://feeny.ai/job/executive-assistant-boston-office-manager-liquid-ai-boston-w7qpd15rv7be) — Boston, MA - [Associate General Counsel, Product & Regulatory](https://feeny.ai/job/associate-general-counsel-product-regulatory-liquid-ai-san-francisco-cv1mmvgze9a6) — San Francisco, CA - [Member of Technical Staff - Inference Systems](https://feeny.ai/job/member-of-technical-staff-inference-systems-liquid-ai-boston-n21ddyv32pqs) — Boston, MA - [Member of Technical Staff - ML Scientist, Japanese Multimodal](https://feeny.ai/job/member-of-technical-staff-ml-scientist-japanese-multimodal-liquid-ai-tokyo-b1sxex046dg8) — Tokyo, Japan - [Member of Technical Staff - Applied ML, Japanese Multimodal](https://feeny.ai/job/member-of-technical-staff-applied-ml-japanese-multimodal-liquid-ai-tokyo-hv79bwy6nmkk) — Tokyo, Japan - [Member of Technical Staff - GPU Infrastructure Engineer](https://feeny.ai/job/member-of-technical-staff-gpu-infrastructure-engineer-liquid-ai-san-francisco-5qry0efpcqvv) — San Francisco, CA