Deploy and benchmark AI and LLM workloads on FuriosaAI's RNGD NPU using the Furiosa SDK.
Originally posted August 22, 2026 by the employer.
About the role
FuriosaAI is looking for a Solutions Architect to act as the primary technical authority in AI and LLM model deployments on RNGD chips and servers. In this role, you will lead proofs of concept (POCs), benchmarking, and debugging to translate RNGD systems into customer deployments.
What you'll do
- Own end-to-end technical enablement for US customers deploying AI models on the 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 evaluations and communicate technical capability to engineering and executive audiences.
- Maintain expertise across FuriosaAI's hardware and software stack and present at technical forums, AI conferences, and customer workshops.
- Onboard and train customers on integration patterns, optimization workflows, and post-purchase best practices.
- Deliver technical feedback from US customers back to Seoul HQ product and engineering teams.
What you'll need
- 2–5 years in a US customer-facing technical role such as Solutions Architect, Sales Engineer, Forward Deployed Engineer, or equivalent at an AI infrastructure, cloud, or semiconductor company.
- Current working knowledge of the AI/LLM landscape, model releases, inference frameworks, and serving stacks.
- Hands-on experience with modern inference stacks such as vLLM, SGLang, TensorRT-LLM, or Triton Inference Server.
- Hands-on experience with agent and orchestration frameworks such as LangChain, LlamaIndex, LangGraph, AutoGen, or MCP-based tooling.
- Proficiency in Python, with hands-on comfort in DNN frameworks including PyTorch or TensorFlow.
- Authorization to work in the US and willingness to travel to customer sites and Seoul HQ periodically.
Nice to have
- Prior experience at an 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 techniques, including quantization, kernel tuning, batching strategies, and memory bandwidth optimization.
- Proficiency in C, C++, or Rust.
- Experience working with distributed or cross-timezone engineering teams.
Skills: Solutions Architect, POC, Inference Frameworks, vLLM, Python, NPU
More semiconductor jobs: Field applications engineer jobs · Santa Clara jobs
First seen in our index 0 days ago, well below the 67-day median for Applications FAE roles.
Applications FAE · AI ML Hardware
|
Open roles in category
122
|
Median days since first seen
67 d
|
Category median salary (all employers)
$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 (122 roles · 22 employers) |
|---|---|---|
| Open roles in this specialty | 4 | 122 |
| Median days since first seen | 0 d | 67 d (+67 d vs this employer) |
| Median salary (USD postings) | n/a | $236k |
Skills observed across this category: Solutions Architect, POC, Inference Frameworks, vLLM, Python, NPU
Who's hiring in this category
- NVIDIA · 66 open roles · median 74 d
- AMD · 12 open roles · median 79 d
- Renesas Electronics · 8 open roles · median 17 d
- Qualcomm · 7 open roles · median 30 d
- Tenstorrent · 7 open roles · median 58 d
- Arm Holdings · 3 open roles · median 17 d
How we counted: 122 open Applications FAE (AI ML Hardware) roles from 22 employers tracked in the SemiconductorJobs index, counted 14 Nov 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there. Days are counted from when our index first saw each posting, not from the employer's original posting date.