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Embedded / Electrical Engineer

Human Archive· Engineering
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San FranciscoINR 2500k - INR 4000kFull-timePosted 3mo ago

About the role

Embedded/electrical engineer to 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 at Human Archive.

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.

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.

Skills

Required:Embedded systems designSoM/SBC bring-upSensor interfacingPower electronicsSignal integrity / EMI
Last updated Oct 25, 2026. Interested? Apply on the company site.
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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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