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.

We need someone who:

• 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

• 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

1. Qualified opportunities convert to technical wins faster, with a measurable improvement in the qualified-to-win rate

2. A library of scalable demos, engagement playbooks, and customer-facing collateral exists and is actively used

3. A structured feedback loop from customer conversations to the product and model teams is established and influencing roadmap decisions