Solutions Architect
Liquid AI
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • United States
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About the role
- 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
Requirements
- 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
Benefits
- 100% of medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO plus company-wide Refill Days throughout the year
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningmodel evaluationmodel deploymentquantizationmodel serving frameworksedge deploymentcustomer engagementtechnical validationROI quantificationsolution architecture
Soft Skills
customer-facing communicationrelationship buildingdiscovery facilitationexecutive presentationthought leadershipcontent creationcollaborationproblem-solvinginfluencingdocumentation