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Confirmed on the employer's own hiring board on Oct 28, 2026. First seen by Alion on May 9, 2026. Human Archive scores A on the Alion truth index.

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Human Archive is building data infrastructure for physical AI, capturing how people perform real-world tasks. It designs its own hardware, deploys cameras and sensors in working environments and trains models on the resulting footage. The data is aimed at robotics teams that lack examples of skilled manual work.
Backed by Y Combinator

About Human Archive

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.

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