Human Archive is a Silicon Valley startup that collects synchronized egocentric video and multi-sensor data from gig-economy workers to train AI and robotics models.
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 The single hardest role on the team. You'll own the entire optics and calibration stack — lens selection, image quality, intrinsics, extrinsics, ISP tuning, multi-camera alignment. End to end. Optics and sensor selection - Lens characterization (MTF, distortion, chromatic aberration) - Sensor variant evaluation and selection - Optical mount tolerance budget with the mechanical team - IR filter and lens shading correction approach Image quality and ISP tuning - AE, AWB, color matrix, gamma, lens shading correction - Multi-camera color and exposure consistency - Image quality validation in mixed real-world lighting Multi-stage calibration pipeline - Camera intrinsics and fisheye distortion modeling - Stereo and multi-camera extrinsics - Camera-IMU spatial and temporal calibration - Magnetometer calibration in the assembled stack Calibration infrastructure - Rig design and fixture builds (turntables, lighting, targets) - Calibration software pipeline - Automated verification and QA systems - Documentation for factory deployment Production and field calibration - Factory-floor calibration procedure - Per-unit calibration QA gating - Field calibration drift monitoring - Sensor / lens transition tooling Required technical experience - Hands-on multi-camera calibration (intrinsics, stereo, multi-rig) - Fisheye distortion modeling (Kannala-Brandt or equivalent) - IMU calibration: bias, alignment, Allan variance, temperature curves - Calibration tooling and infrastructure for production - Comfortable in both the optics lab and the calibration software stack Strong plus - OpenCV, Kalibr, or similar calibration frameworks - ISP tuning on Qualcomm, NXP, or Ambarella platforms - AR/VR, autonomous vehicle, or research-grade capture device background - Camera-IMU temporal alignment - Manufacturing calibration line experience - Uncertainty quantification and error analysis About this role Your work determines whether thousands of hours of captured data is usable. Bad calibration is silent — data passes acceptance and fails downstream model training months later. The candidate pool is small. We pay top of band.