2 дня назад
Solutions Architect (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Solutions Architect (AI): Building customer-specific demos, proofs of concept, and production solutions around efficient AI models for edge devices, mobile, embedded systems, and on-prem infrastructure with an accent on technical discovery, model deployment, and ROI-driven architecture. Focus on fine-tuning and evaluating small models, optimizing latency and memory usage, and turning customer engagement insights into reusable solutions and product roadmap feedback.
Location: Hybrid in San Francisco or Boston, United States
Company
, spun out of MIT CSAIL, builds general-purpose AI systems optimized for low latency, minimal memory usage, privacy, and reliability across data center and on-device deployments.
What you will do
- 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 by mapping customer architectures to Liquid solutions and presenting competitive positioning against open-source and incumbent models.
- Quantify ROI for cost optimization and new-experience use cases, including deployments on edge, mobile, embedded, and on-premise infrastructure.
- Partner with product and research teams to communicate customer friction, evaluation failures, and capability gaps.
- Create reusable reference architectures, solution primitives, demo components, engagement playbooks, and vertical-specific patterns.
Requirements
- Hands-on applied ML experience in customer-facing work, including demos, prototypes, or production integrations.
- Experience owning technical customer engagements across both pre-sales and post-sales stages.
- Strong communication skills for technical discovery, relationship building, and executive presentations.
- Understanding of AI architectures and deployment tradeoffs, including token efficiency, on-device versus cloud deployment, model size versus latency, and open-weight versus proprietary models.
- Ability to work in a hybrid arrangement in San Francisco or Boston.
Nice to have
- Experience deploying small or efficient models in edge, on-device, or latency-constrained environments.
- Knowledge of quantization methods such as INT4, INT8, GGUF, and AWQ.
- Experience with vLLM, TensorRT-LLM, llama.cpp, or hardware-aware optimization.
- Experience designing and debugging model evaluations and diagnosing differences between benchmark and production performance.
- Technical writing, thought leadership, or industry event presentation experience.
Culture & Benefits
- Opportunity to build the solutions architecture function and define technical go-to-market practices.
- Direct influence on product direction with access to the founding team.
- Competitive base salary with equity.
- Company-paid medical, dental, and vision premiums for employees and dependents.
- 401(k) matching up to 4% of base pay, unlimited PTO, and company-wide Refill Days.
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