Human Archive is a research lab backed by Y Combinator focused on modeling human embodied intelligence.
Humans are the most sophisticated biological systems we have ever observed, yet we still do not fully understand ourselves. Research into human physical intelligence — including the human hand, proprioception, and vision — remains largely unsolved. Our mission is to recover human embodied intelligence as a learned model. To achieve this, we build custom hardware products, deploy them globally at scale, and publish research. Today, our data is used for robotics and world modeling, but the broader opportunity is advancing scientific research into intelligence itself.
Founded by Stanford and UC Berkeley researchers, we are lean, deeply 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.
We are building the infrastructure to accelerate that transition by assembling the Human Archive mafia. 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 help reshape physical labor and work on problems that matter at civilizational scale, join us.
What you'll work on
We're hiring an electrical engineer to lead PCB and power systems for our wearable capture hardware. Dense, high-speed, mixed-signal work in tight wearable form factors.
Compute module integration
Custom carrier boards for ARM compute modules
Breakout and routing of high-speed interfaces from compact modules to custom peripherals
Bring-up coordination with firmware
High-speed PCB design
End-to-end ownership of rigid-flex and high-density boards
High-bandwidth interfaces (MIPI CSI-2, SerDes, USB 3.x, PCIe, UFS)
EMI, crosstalk, and impedance control in space-constrained designs
Power architecture and battery systems
Power Delivery Networks for double-digit-watt wearable loads
Battery management for field-swappable cell architectures
Fuel gauging and runtime estimation
Transient load behavior and power sequencing
Sensor and peripheral integration
Multi-camera array electrical integration
Inertial, magnetic, acoustic, and environmental sensor wiring
Low-noise analog rails and shared precision clock distribution
Bring-up, debugging, and validation
Board bring-up alongside firmware
Signal integrity, power integrity, and timing diagnosis
EMC pre-scans and compliance support
Required technical experience
Strong PCB design in Altium, Cadence Allegro, or KiCad
High-speed digital design: differential pair routing, impedance control, stack-up
SI/PI simulation and validation
Compute module / SoM carrier board experience
Lab debugging instincts (scope, logic analyzer, network analyzer)
Strong plus
Multi-camera array design (3+ concurrent cameras)
High-speed SerDes routing
ARM SoC carrier work (Qualcomm, NXP, or similar)
Wearable / body-worn product background
Hot-swap power design
FCC / CE / RoHS compliance and DFM handoff experience
About this role You own electrical design end-to-end — schematic through bring-up through production-ready DFM. The interface stack is broad, the form factor is tight, and the boards you design end up on real users. Mistakes show up in the field within weeks. You'll iterate fast.
Skills Required
- Strong PCB design in Altium, Cadence Allegro, or KiCad
- High-speed digital design: differential pair routing, impedance control, stack-up
- SI/PI simulation and validation
- Compute module / SoM carrier board experience
- Lab debugging instincts (scope, logic analyzer, network analyzer)
What We Do
Human Archive is a data infrastructure company that collects, labels, and synchronizes multimodal data (video, sensor, audio) to create datasets for training embodied AI and robotics systems. Founded by researchers from Stanford and Berkeley, the company aims to advance robotics foundation models by capturing real-world physical data, helping to automate manual labor and improve understanding of human cognition and spatial computing.








