QA / Hardware Test 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 hardware test engineer will own quality and validation across the full hardware stack — electronics, firmware, and system integration. This role goes well beyond test execution: you will build the test infrastructure, author reliability plans, debug failure modes, and drive corrective action.
You will receive direction from the Head of Engineering and work closely with the electrical and firmware engineers.
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.
Required 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.
Preferred Qualifications
Experience with high-speed interfaces (USB, SPI, I2C, UART, CSI) and signal integrity basics.
Experience with power systems testing
- battery-powered devices, DC/DC rails, brownout behavior.
- Experience building test fixtures, cable harness tests, and manufacturing test flows.
- Familiarity with reliability test methods: thermal cycling, vibration, ingress/sweat exposure.
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