Firmware Engineer
About the role
Firmware engineer responsible for real-time firmware on a battery-powered wearable platform, including deterministic sensor acquisition, timebase synchronization, system state control, and MCU-to-embedded Linux communication.
Responsibilities
- Design and maintain firmware for a dedicated MCU responsible for aggregating distributed sensors — implementing robust SPI and peripheral drivers and ensuring stable high-frequency acquisition.
- Architect and implement the system timebase and synchronization strategy across multiple sensor domains, including deterministic event handling and timestamp propagation.
- Develop hardware-level control logic for user inputs and system indicators with interrupt-driven reliability.
- Design boot, shutdown, and fault-recovery mechanisms: watchdog strategies, brownout handling, corruption prevention during unexpected power events.
- Implement a deterministic communication boundary between MCU and the embedded Linux compute layer, including packet structures, buffering strategies, and error handling.
Qualifications
- 3-6 years of embedded firmware development experience.
- Strong proficiency in C or C++ for MCU environments.
- Experience with STM32 or equivalent modern MCU platforms.
- Deep understanding of SPI, I2C, UART, and interrupt-driven design.
- Experience building timing-sensitive firmware and deterministic data pipelines.
- Hands-on debugging with oscilloscopes, logic analyzers, and similar lab tools.
Skills

About Human Archive
Multimodal data provider for robotics and world modeling
We’re archiving the physical world for embodied intelligence by collecting and labeling aligned multimodal data. To build dexterous and perceptive robots that generalize robustly, we need massive amounts of real-world data across multiple modalities and environments. We have thought deeply about the fine line between biomimicry and its application to humanoid systems. Based on this research, we design and deploy custom hardware across residential and manufacturing settings. We then post-process the resulting data through internal QA, anonymization, and annotation pipelines to deliver diverse, high-fidelity datasets at scale to frontier labs developing robotics foundation models and general-purpose robotics companies. We believe we are at a historic inflection point, with a unique opportunity to leave a dent on humanity and reshape physical labor markets forever. That's why our team dropped out of Stanford and Berkeley and moved to Asia to collect the world’s largest annotated multimodal dataset.
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