5 months ago
Responsibilities
- Own customer engagements from qualified opportunity through technical validation, go-live, and ongoing delivery.
- Build customer-specific demos and proofs of concept using Liquid models and LEAP for fine-tuning, domain adaptation, and evaluation.
- Lead technical discovery, map customer architectures to Liquid solutions, drive competitive positioning, and quantify ROI.
- Co-own the product-field feedback loop by documenting friction patterns, evaluation failures, and capability gaps for product and research teams.
- Create reusable reference architectures, solution primitives, demo building blocks, engagement playbooks, and vertical-specific solution patterns.
- Help establish and scale the solutions architecture function, processes, and customer-facing assets.
Requirements
- Hands-on applied ML experience in customer-facing contexts, including building demos, prototypes, or production integrations.
- Experience owning technical customer engagements end-to-end across pre-sales and post-sales.
- Strong ability to lead discovery, build relationships with technical and business buyers, and present to executives.
- Understanding of AI architecture and deployment tradeoffs, including token efficiency, on-device versus cloud deployment, model size versus latency, and open-weight versus proprietary models.
- Familiarity with small or efficient model deployment in edge, on-device, or latency-constrained environments is preferred.
- Experience creating technical thought leadership content, technical blogs, or industry presentations is preferred.
- Familiarity with quantization methods including INT4, INT8, GGUF, and AWQ; model-serving frameworks including vLLM, TensorRT-LLM, and llama.cpp; and hardware-aware optimization is preferred.
- Experience designing and debugging model evaluations and diagnosing differences between benchmark and production performance is preferred.
Benefits
- 100% of medical, dental, and vision premiums paid for employees and dependents.
- 401(k) matching up to 4% of base pay.
- Unlimited PTO and company-wide Refill Days.
- Opportunity to build the solutions architecture function and influence product direction with access to the founding team.
Tech Stack
Jupyter Notebook
Categories
Solutions Engineering
About Liquid AI
Liquid AI builds general-purpose AI systems that run efficiently from data center accelerators to on-device hardware, emphasizing low latency, memory efficiency, privacy, and reliability. The company partners with enterprises in consumer electronics, automotive, life sciences, and financial services to deploy and benchmark models for real-world workloads. Founded in 2023 out of MIT CSAIL and headquartered in Cambridge, Massachusetts, it is privately held.