Software FAE (Field Application Engineer)

This team is not like the FAE teams you've worked on before

We're putting our entire FAE workflow on AI rails: tickets are auto-classified and summarized on arrival, and the knowledge base is continuously maintained by an AI agent. On top of that, we're building an AI-driven automated test bench to run the repetitive checks.

You aren’t just joining an FAE team to troubleshoot—you’re here to pioneer an AI Agent Workflow. You will be equipped with a complete AI toolchain, leverage a debugging knowledge base that compounds over time, and have the opportunity to architect a state-of-the-art AI system.

What we're building

◆An automated ticket workflow — auto-assignment plus a continuous knowledge-digest cycle, so every resolved ticket feeds the knowledge base.
◆An AI-driven test bench — automating the repetitive checks and turning debugging judgment into reusable test rules.
◆A human / AI co-working model — engineers and AI agents working side by side: AI handles the repetitive load, humans own the judgment calls.

What you'll do

◆Serve as the technical owner of the NVIDIA Jetson / Edge AI product line, acting as the team's L2 escalation endpoint (the next step up is the vendor's R&D).
◆Deep debugging on Jetson platforms: BSP, kernel/driver, bring-up, and root-cause analysis of CUDA/TensorRT deployment issues.
◆Partner with and build the AI automated test bench — continuously translating your debugging judgment into automated test rules.

Requirements

◆3+ years in embedded Linux development or technical support (kernel, device tree, board bring-up).
◆Familiar to at least one coding language (bash / C /C++…etc)
◆Hands-on experience with NVIDIA Jetson or comparable edge AI platforms — you've debugged real production issues, not just run demos.
◆Curiosity and openness toward AI tools — we don't require you to be an expert already, but we do require you to be willing to make them part of how you work every day.

Nice to have

◆Practical experience with CUDA / TensorRT / DeepStream, and AI applications.
◆Built automated tests, CI pipelines, or test fixtures.
◆x86 platform experience (BIOS/BMC) — the team needs cross-platform backup.
◆Have led or mentored junior engineers.
◆Able to face customer engineers independently and communicate technically in English

AI skills (better to have)

◆Prompt engineering for technical / debugging workflows — getting reliable, structured output from LLMs.
◆Familiarity with RAG or knowledge-base retrieval systems, and with AI agents / tool-calling (e.g. MCP-style integrations).
◆Scripting against LLM APIs (e.g. Python + Anthropic / OpenAI SDK) to automate parts of your own workflow.
◆Running or optimizing LLMs / vision models on the edge (e.g. TensorRT-LLM, on-device inference) — a natural bridge between your Jetson depth and our AI direction.