AI Software Engineer, Hardware Systems

As an AI Software Engineer, you’ll design and ship production LLM-powered systems that help hardware teams move faster - from early design and prototyping through validation, bring-up, and operations.

This is a deeply hands-on software role. You’ll spend most of your time writing Python and backend code: building RAG pipelines, standing up agentic workflows with frameworks like LangChain/LangGraph and LlamaIndex, integrating with internal services, and deploying robust, observable services to the cloud or on-prem.

You’ll also bring real hardware intuition to the table. Whether your background is in PCB and electronics design, RF/wireless systems, embedded/firmware, or chip/IC design, you’re comfortable reading schematics, understanding constraints, and speaking the language of hardware engineers. Your work will turn noisy engineering data and tool outputs into reliable AI agents that help world-class AR/VR and wearable teams design, debug, and ship hardware faster.

• Integrate AI solutions seamlessly with existing electronic and engineering infrastructures, ensuring reliability and performance.
• Deploy and fine-tune domain-specific AI models, particularly LLM-powered architectures, in cloud environments to enhance operational efficiency and insights.
• Ship to cloud: containerize services, automate CI/CD, provision runtime/inference services, and operate them with basic observability and cost controls.
• Leverage LangChain/LangGraph and/or LlamaIndex to build agentic workflows, tools, and routers; evaluate models and prompts with repeatable harnesses.
• Stand up and tune vector/hybrid search with appropriate metadata, hybrid scoring, and guardrails.
• Collaborate with electrical/RF/firmware teams; translate problem statements into AI features, validate with real datasets and on‑bench workflows.

• 2–3+ years of professional software engineering; 1+ years building and deploying LLM‑based apps in production or serious pilots.
• Strong Python; experience shipping services (FastAPI/Flask), writing clean APIs, and working in Linux/Git/Docker.
• Hands‑on RAG experience (indexing/embedding, retrieval, reranking, evaluation) and one or more vector stores.
• Practical use of LangChain/LangGraph and/or LlamaIndex for orchestration and data frameworks.
• Cloud familiarity (AWS/GCP/Azure) for deploying model‑backed services and data infra.
• Hardware literacy: can read schematics/PCBs, reason about components/BOM, and converse fluently about electronics; exposure to at least one of RF/wireless/PCB bring‑up/test.

• MS in Electrical Engineering, Computer Engineering, Computer Science, or related.
• Experience across ECAD/EDA and hardware workflows (Altium/KiCad; Cadence Allegro/OrCAD or Virtuoso; Siemens Xpedition; Synopsys/Cadence EDA).
• Wireless/RF background (e.g., BLE/Wi‑Fi/LoRa/5G fundamentals; link budgets, S‑parameters, impedance, SDR).
• LLM depth: agent/tool use, function‑calling/routing, evaluation frameworks (e.g., LangSmith/Ragas/DeepEval), prompting at scale, caching/cost controls, LoRA/fine‑tuning, multimodal (text+image/OCR).
• MLOps/DevOps: Kubernetes/KServe, Terraform, metrics/logging/tracing, secrets management; experience operating inference stacks (e.g., vLLM/TensorRT‑LLM, OpenAI/Anthropic/Azure/Open‑source models).