Lead Embedded Firmware Engineer

You will lead the firmware architecture and development for a breakthrough stealth-stage neurotechnology and brain-computer interface (BCI) startup building an AI-powered neural interface platform. This role owns the distributed-compute firmware ecosystem powering media and connectivity, biosignal acquisition, and always-on MCUs responsible for power management, privacy, and thermal control. The platform is redefining the future of human communication and human-computer interaction through tightly integrated wearable hardware, low-power embedded systems, and real-time intelligent processing. Lead/Architect firmware across the distributed-compute platform: RTOS choice, task structure, inter-processor protocols, OTA, and time sync. Own the runtime that hosts every subsystem, audio DSP, camera capture pipeline, biopotential acquisition, through specified interfaces with each subsystem lead. Own the media and connectivity MCU pipeline, camera capture, audio runtime, wake-word, Wi-Fi streaming, and onboard logging, concurrently within strict CPU and memory budgets. Drive low-power firmware on the always-on MCU: state machines for standby, assist, and continuous modes; hardware-enforced privacy; thermal throttling. Build the multi-MCU time-sync layer that lets us correlate EEG, audio, and camera data downstream. Establish the firmware engineering practices that scale: build and release pipelines, on-device telemetry, automated test, OTA with safe rollback, field debug tooling. Partner with the EE lead on hardware bring-up and boot path; with the reliability lead on field telemetry, error handling, and diagnostic surfaces. Bring up ASICs in collaboration with the EE and silicon teams Ship the product by the end of year and build and lead the firmware team as we scale to production. Responsibilities 10+ years of embedded firmware engineering, with at least one shipped consumer product where you owned firmware architecture end-to-end. Deep expertise across embedded RTOSes and bare-metal ARM Cortex-M, with familiarity across at least two ecosystems (e.g., Zephyr, FreeRTOS, ESP-IDF, ThreadX, NuttX). Hands-on experience hosting real-time DSP runtimes alongside wireless connectivity on resource-constrained MCUs — integrating algorithms owned by other teams. Strong background in multi-radio coexistence (Wi-Fi + BLE), low-power state-machine design, and OTA with safe rollback. Comfortable in the lab with JTAG/SWD, logic analyzers, and protocol sniffers — able to drive bring-up from first power-on through end-to-end functional demos. Preferred Skills Deploying neural network inference to low-power MCUs or dedicated AI accelerators — model conversion, quantization, runtime integration. Familiarity with on-device inference frameworks and edge AI runtimes. Custom AI accelerator silicon, neuromorphic compute, or in-memory-compute platforms. On-device wake-word or always-on voice activation engines. Integrating biopotential acquisition over standard sensor buses. #J-18808-Ljbffr OSI Engineering