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QA / Hardware Test Engineer

Human Archive· Engineering
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Hybrid (San Francisco, CA)INR 1800k - INR 3000kFull-timePosted 3mo ago

About the role

Hardware QA engineer responsible for quality and validation across the full hardware stack (electronics, firmware, system integration) at Human Archive. Build test infrastructure, develop reliability plans, debug failures, and drive corrective actions.

Responsibilities

  • Define test plans, matrices, and acceptance criteria aligned with product requirements; build and maintain verification checklists for electrical, firmware, and system-level functionality.
  • Execute validation testing across hardware, firmware, and end-to-end system integration; develop repeatable bring-up and regression procedures for new builds.
  • Build test fixtures, harnesses, and bench setups for repeatable measurements; design and improve manufacturing test procedures and go/no-go checks.
  • Create test scripts in Python for logging, validation, and automated regression testing.
  • Perform structured debugging using oscilloscopes, logic analyzers, multimeters, and serial logs; write precise bug reports with reproduction steps and recommended next actions.
  • Own reliability testing: burn-in, stress, connector/cable durability, environmental exposure.
  • Track defect trends, implement corrective/preventive actions, and maintain QA traceability.

Qualifications

  • 6-8 years of experience in QA, validation, test engineering, or reliability engineering in hardware/embedded contexts.
  • Strong understanding of embedded systems testing across electronics, firmware, and integration.
  • Hands-on lab experience with oscilloscopes, logic analyzers, multimeters, and serial debug tools.
  • Experience designing and executing structured test plans and regression processes.
  • Scripting ability in Python for automated validation, data parsing, and reporting.

Skills

Required:Test planningPython scriptingHardware debugging
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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