IoT Hardware Physical AI Engineer

Builds the hardware POCs and physical-world integrations + connected products, sensors, proximity flows. What you’ll do - Ship POC hardware projects to Early Stage connected products - Integrate BLE (mesh, beacon, GATT), NFC (HCE, peer-to-peer, tag programming), and LoRaWAN (TTN, ChirpStack) with cloud and client systems. - Full-stack: firmware on the device, edge compute, backend APIs, lightweight web/app dashboards so a non-technical client can see the POC live. - Bridge physical hardware to AI agents — sensor data in, agent decisions out. - Partner with AI Developers to translate physical-world events into the same agent infrastructure they already run. - Ship demo-ready POCs the can put on a desk, in a store, or on a trade-show floor in 15-30 days. What you bring - Claude Code ninja. You drive AI-pair coding fluently — full-stack features shipped in days, not weeks. Non-negotiable. - Production embedded experience (Embedded C/C++, Rust, MicroPython) on ESP32, Nordic nRF, STM32, or Raspberry Pi. - Strong BLE protocol fluency — mesh, beacon, advertising, GATT services, pairing. - NFC working knowledge: HCE on Android, peer-to-peer, tag write/read, NDEF. - LoRaWAN deployment experience: device classes, gateways, network servers (TTN, ChirpStack, Helium). - Full-stack web/app: TypeScript, Node, Next.js, React Native. Can ship the dashboard the client sees, not just the firmware. - Calm with prototype-grade hardware — soldering, breadboarding, basic oscilloscope and logic-analyzer work. Nice to have - PCB design (KiCad, Altium) — even at the schematic-and-handoff level. - Industrial design / 3D modeling (Fusion 360, OnShape) for enclosures. - AWS IoT Core or Azure IoT Hub for production-grade fleets. Edge ML / computer vision (TensorFlow Lite, ONNX Runtime, edge inference). - Past work that went from POC to manufacturing (DFM mindset). - Bluetooth Auracast, UWB, Matter / Thread — emerging protocols.