About the JobFuriosaAI is looking for a Solutions Architect to bring the full potential of our powerful RNGD chips/servers to our customers by acting as the primary technical authority in AI/LLM model deployments. From running POCs to benchmarking and debugging, you will translate RNGD's powerful system to real-world deployments of customers' models, empowering customers with FuriosaAI's powerful solutions.
If you are interested in providing the technical expertise in challenging the current status-quo of AI infrastructure in real-world environments, join us in our path to a sustainable future of AI.
What You'll Do- Own end-to-end technical enablement for US customers deploying AI models on FuriosaAI's RNGD NPU using the Furiosa SDK
- Develop POCs, benchmarking studies, and live debugging sessions directly in customer environments
- Act as the technical authority to the US BD/Sales team during pre-sales and enterprise evaluations; translate deep technical capability into business value for engineering and C-suite audiences
- Develop deep, current expertise in FuriosaAI's hardware and software stack and demonstrate it at US technical forums, AI conferences, and customer workshops
- Onboard and train customers on integration patterns, optimization workflows, and best practices post-purchase
- Serve as a technical feedback loop from US customers back to Seoul HQ product and engineering teams
Qualifications- 2-5 years in a US customer-facing technical role: Solutions Architect, Sales Engineer, Forward Deployed Engineer, or equivalent at an AI infra, cloud, or semiconductor company
- Actively current on the AI/LLM landscape - tracking model releases, inference frameworks, and serving stack evolution in real time
- Hands-on experience with modern inference stacks: vLLM, SGLang, TensorRT-LLM, Triton Inference Server, or similar
- Hands-on experience with agent and orchestration frameworks: LangChain, LlamaIndex, LangGraph, AutoGen, or MCP-based tooling
- Proficiency in Python; comfortable with DNN frameworks (PyTorch, TensorFlow)
- Strong written and verbal communication - able to engage credibly with ML engineers at frontier labs and VP/C-suite executives
- Authorized to work in the US; able to travel to customer sites and to Seoul HQ periodically
Preferred Qualifications- Prior experience at a US AI chip company, cloud silicon team, or AI infrastructure startup
- Familiarity with NPU/GPU accelerator ecosystems, PCIe integration, and data center hardware deployment
- Experience with inference optimization: quantization, kernel tuning, batching strategies, memory bandwidth optimization
- Proficiency in C, C++, or Rust
- Experience working with distributed or cross-timezone engineering teams
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