Hiring: Solutions Architect Company: Liquid AI Location: San Francisco, CA Job Posted Time: 2026-09-12 08:36:29 Target Skills & Keywords : C++, Embedded Systems, Fine-tuning, Jupyter Notebook, LLM, TensorRT, vLLM About the job Experience: •Applied ML skills: hands-on experience working with ML models in customer-facing contexts (building demos, prototypes, or production integrations) •Pre-sales and post-sales experience: you have owned technical customer engagements end-to-end, not just the pitch •Strong customer-facing communication: you can run discovery, build relationships with technical and business buyers, and present to executives •Understanding of AI architectures and deployment tradeoffs: token efficiency, on-device vs. cloud, model size vs. latency, open-weight vs. proprietary Required Skills: •Liquid AI is building a solutions architecture function from scratch. You will be one of the first SAs, working directly with the Head of Solutions Architecture and across the go-to-market org to own customer engagements end-to-end. •Our models are purpose-built for environments where memory, latency, and power are binding constraints - edge devices, mobile, embedded systems, and on-prem infrastructure where frontier models simply cannot run. You will work at this boundary every day. •Customers range from AI-native companies to enterprise organizations exploring AI for the first time. Your job is to bridge the gap between what our models can do and what customers believe is possible, then deliver on that promise from technical validation through go-live. •Technical builder: You can download a model, build a demo, and present it to a customer. You are as comfortable in a Jupyter notebook as you are in a boardroom. •Creative problem solver: You see opportunities where customers see limitations. You can take a small, efficient model and show an enterprise why it changes their cost structure or enables something they did not think was possible. •End-to-end owner: You do not draw a line between 'pre-sales' and 'post-sales.' You own the outcome from first call to go-live and beyond. •Org builder: You want to build a function, not inherit one. You will create playbooks, demo libraries, and engagement processes that scale as the team grows. •Imagination-gap closer: Enterprise buyers often cannot envision what a fine-tuned small model can do at middleware speeds. You don't just demo—you reframe what's possible on hardware they already own. Qualifications: •Operational familiarity with small or efficient model deployment (edge, on-device, latency-constrained environments) •Track record of creating thought leadership content, technical blogs, or presenting at industry events •Operational familiarity with efficient model deployment: quantization (INT4/INT8, GGUF, AWQ), model serving frameworks (vLLM, TensorRT-LLM, llama.cpp), and hardware-aware optimization for edge or latency-constrained environments •What Success Looks Like (Year One) •Qualified opportunities convert to technical wins faster, with a measurable improvement in the qualified-to-win rate •A library of scalable demos, engagement playbooks, and customer-facing collateral exists and is actively used •A structured feedback loop from customer conversations to the product and model teams is established and influencing roadmap decisions Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!