Solutions Architect - US - Santa Clara, United States
Originally posted 18 May 2026 by the employer.
Enable customers to deploy AI models using FuriosaAI's RNGD NPU and Furiosa SDK.
About the role
This role is responsible for the technical enablement of US customers deploying AI/LLM models on FuriosaAI's RNGD NPU using the Furiosa SDK.
What you'll do
- Own end-to-end technical enablement for US customers deploying AI models on FuriosaAI's RNGD NPU.
- 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.
- 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 product and engineering teams.
What you'll need
- 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.
- Expertise in the AI/LLM landscape, tracking model releases, inference frameworks, and serving stack evolution.
- 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).
- Authorized to work in the US; able to travel to customer sites and to Seoul HQ periodically.
Nice to have
- 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.
Skills: AI/LLM landscape, vLLM, SGLang, TensorRT-LLM, Triton Inference Server, LangChain
This role has been open 84 days — exactly the 84-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
507
|
Median days open
84 d
|
Median salary
$236k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | FuriosaAI | All employers we track in this specialty (507 roles · 77 employers) |
|---|---|---|
| Open roles in this specialty | 6 | 507 |
| Open roles in the wider Software, Firmware & Systems family | 26 | 6118 · 145 employers |
| Median days open | 83 d | 84 d (−1 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: AI/LLM landscape, vLLM, SGLang, TensorRT-LLM, Triton Inference Server, LangChain
Who's hiring in this category
- Qualcomm · 99 open roles · median 115 d
- NVIDIA · 91 open roles · median 54 d
- AMD · 41 open roles · median 82 d
- Micron Technology · 28 open roles · median 61 d
- Analog Devices · 19 open roles · median 37 d
- Mobileye · 17 open roles · median 81 d
How we counted: 507 open AI/ML Hardware Engineering (AI ML Hardware) roles from 77 employers tracked in the SemiconductorJobs index, counted 19 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.