Liquid AI

Solutions Architect

San Francisco

Sponsorship not specifiedDetected 101 days ago

Machine LearningLLMsSalesEmbedded SystemsSASResearchLeadershipCommunicationPublic Speaking

About the role

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.

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.
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.
You do not draw a line between 'pre-sales' and 'post-sales.' You own the outcome from first call to go-live and beyond.
You want to build a function, not inherit one.
You will create playbooks, demo libraries, and engagement processes that scale as the team grows.
Build the function: You are defining how Liquid goes to market technically, with direct influence on product direction and access to the founding team.

You are as comfortable in a Jupyter notebook as you are in a boardroom.
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.
you have owned technical customer engagements end-to-end, not just the pitch

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

Pre-sales and post-sales experience: you have owned technical customer engagements end-to-end, not just the pitch
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
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
token efficiency, on-device vs. cloud, model size vs. latency, open-weight vs. proprietary

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

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
Time Off: Unlimited PTO plus company-wide Refill Days throughout the year

You see opportunities where customers see limitations.
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.
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.

This listing is sourced directly from Liquid AI's careers page and normalized into a canonical job model.