Solutions Architect - US

About the Job

FuriosaAI 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

Contact

• View email address on click.appcast.io