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
You will design and build the physical architecture of a load-bearing wearable system — enclosures, mounting structures, modular docking mechanisms, and cable routing — with direct technical direction from the Head of Engineering.
This is a hands-on, execution-focused role. You will prototype fast, iterate based on real wear testing, and maintain high standards of mechanical quality and repeatability.
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.
Location
IN
Experience
3+ years
Last stage
Seed
Investors
No applications, no recruiter spam. Just the intro.
A few questions to make sure this role is the right shape for you. Two minutes.
I write the intro, send it to the founder, and handle the back-and-forth.
If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.