Embedded / Electrical Engineer
at Human Archive — Multimodal data provider for robotics and world modeling
What the role involves
About Human Archive
Human Archive is a robotics data lab founded by Stanford and UC Berkeley dropouts. We work alongside frontier robotics labs and foundation model research groups to collect large-scale, real-world, annotated multimodal datasets of humans performing everyday tasks across household and industrial environments.
We are lean, technical, and operate at extreme speed, taking on unglamorous and conventionally impossible problems that directly unlock step-function gains in model capability.
The deployment of capable humanoids at scale will permanently redefine human labor. Undesirable physical work will disappear, and human effort will shift toward a new era of abundant creativity. This shift is inevitable, and we are building the infrastructure to accelerate it.
We are assembling the best team to solve the hardest problems in embodied intelligence. You will own meaningful systems from day one and see your work directly impact model capabilities. This is a once-in-a-generation inflection point. If you want to leave your dent on humanity and reshape physical labor markets forever, join us!
About the Role
The embedded/electrical engineer will design, validate, and harden the electrical and embedded hardware foundation of a battery-powered wearable platform — including power architecture, sensor integration, signal integrity, and full-system validation.
You will work closely with the Head of Engineering, who defines system requirements and architecture.
Responsibilities
Design and validate a multi-rail battery-powered architecture, including power budgeting, DC/DC regulation, protection design, and load stability under peak compute and sensor load.
Bring up and integrate an embedded compute platform (SoM or SBC), including high-speed sensor interfaces and storage subsystems.
Integrate distributed sensors across physical segments, designing robust bus topologies and ensuring accurate time-stamping and data alignment.
Validate signal integrity and grounding across harnessed interconnects; define connector standards and pinout documentation.
Design system-level controls and indicators with reliable interrupt behavior.
Lead full-system validation: EMI, transient response, power stability, environmental durability.
Required Qualifications
3-7 years of experience in embedded systems or hardware integration.
Demonstrated experience bringing up SoM or SBC platforms under real-world load.
Strong understanding of battery-powered, multi-rail power systems and protection strategies.
Hands-on experience integrating sensors over SPI, I2C, UART, USB, or similar interfaces.
Experience debugging signal integrity, EMI, power instability, or synchronization issues.
Proficient with oscilloscopes, logic analyzers, and standard lab instrumentation.
Preferred Qualifications
Experience integrating high-bandwidth camera systems or synchronized multi-sensor platforms.
Background in robotics, drones, motion capture, or wearable hardware.
Familiarity with deterministic time-stamping and multi-sensor alignment.
Exposure to regulatory and compliance considerations for electronic systems.
To Apply
Resume (PDF)
Portfolio or GitHub (if applicable)
About Human Archive
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.
Full Human Archive profile