Solutions Architect

Liquid Ai Solutions Architect 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. 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. What We're Looking For We need someone who: 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. The Work Own customer engagements end-to-end: from qualified opportunity through technical validation, go-live, and ongoing delivery across all customer segments Build customer-specific demos and proofs-of-concept using Liquid models (including LEAP for fine-tuning, domain adaptation, and evaluation) to drive technical wins Lead technical discovery: map current-state customer architectures to Liquid solutions, drive competitive positioning against open-source and incumbent models, and quantify ROI for both cost-optimization and new-experience use cases Co-own the product-field feedback loop: document friction patterns, eval failures, and capability gaps from engagements and partner with product and research to influence roadmap Turn engagement learnings into reusable assets: reference architectures, solution primitives, demo building blocks, engagement playbooks, and vertical-specific solution patterns across Liquid's priority industries Desired Experience Must-have: 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 Nice-to-have: 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 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 Experience designing and debugging model evaluations—you understand why benchmark results can diverge from production performance and know how to diagnose the root cause 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 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 Liquid AI