The posting
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.
About the role
We operate a fleet of camera and sensor devices in the field at scale. They generate large volumes of recordings on removable media, and every recording has to reach cloud storage intact, correctly identified, and traceable back to the exact device, card and operator that produced it.
This role owns the technical layer underneath that pipeline. Day-to-day offload is run by an operations lead and their team; you are the person who makes their work possible and correct. You own the ingest stations, the tooling that runs on them, the hardware fleet, quality control, and the standards everyone follows. When something fails in a way the operations team cannot resolve, it comes to you.
It is hands-on. You will spend time at ingest stations, in the quality control area, and at deployment sites, and you will write scripts to make all of it less manual.
It suits someone comfortable with a Linux terminal and a screwdriver in the same afternoon, who finds an unaccounted-for storage card genuinely intolerable.
WHAT YOU’LL DO
Own the ingest pipeline (the operations team runs it)
- Build, configure and maintain ingest stations — hardware, operating system, tooling — and replicate that build reliably across locations
- Own the ingest tooling in the field: deploy updates, fix what breaks, and carry requirements back to the software team
- Define the verification and media lifecycle rules the operations team follows — what must pass before a card is released for reuse, and what happens when it doesn’t
- Act as escalation for failed and partial ingests: determine whether the cause is media, device, station, tooling or procedure, and fix the underlying issue rather than the instance
- Audit that the process is actually being followed, and close the gaps you find
Own the hardware fleet
- Maintain inventory of capture devices, memory cards and drives across locations, with full chain of custody
- Run reconciliation and investigate discrepancies to root cause rather than writing them off
- Specify and evaluate hardware — cards, readers, hubs, enclosures, drives — and manage returns and warranty claims with suppliers
- Plan capacity: how many cards, how many drives, what rotation, with margin
Quality control
- Investigate recording failures with controlled tests rather than guesswork; isolate whether a fault sits in the device, the media, the station or the procedure
- Decide what gets escalated, repaired or retired, and document the evidence behind the call
- Define capture configuration standards and verify device settings in the field
Support the teams
- Be the technical escalation point for the operations lead, the quality control team and field operators
- Train operators on tooling and hardware, and write procedures a new joiner can follow unsupervised
- Travel to deployment sites as needed
Report
- Produce recording yield, data loss and operator performance reporting
- Be able to say whether a bad number reflects a real failure or an artefact of how it was measured
Automate
- Write Python and shell scripts to remove manual steps from the workflow
- Work with the software team on tooling requirements, and file precise bug reports when the software gets in the way
WHAT WE’RE LOOKING FOR
- A technical background — a degree in engineering or computer science, or equivalent hands-on experience. This is an engineering role that happens to involve hardware and field work, not a coordination role with a technical veneer
- Comfort in Linux: mounting and formatting media, filesystems, permissions, scheduled jobs, reading logs
- Working Python — enough to script a batch job, walk a directory tree, or call an API
- Fluent use of AI tools — Claude, Claude Code, Cowork or equivalent — as part of how you work day to day. We expect you to use them to write scripts, investigate failures and draft procedures faster than you would alone, and to know where their output needs checking before it reaches a station or an operator
- Hardware troubleshooting instinct: forming a hypothesis, testing it, eliminating causes one at a time
- Real care about data integrity and chain of custody, and the discipline to follow a verification step even when it is inconvenient
- Clear written English — you will write procedures other people depend on
- Willingness to work with physical equipment and travel to sites
- Comfort in an environment where the process may not exist yet and you are expected to define it
NICE TO HAVE
- Experience with cameras or sensor hardware
- SQL, or experience querying an internal API for reporting
- Storage systems — RAID, ZFS, network-attached storage administration
- Prior work in data collection, field operations, logistics or laboratory operations
- Barcode or asset-tracking systems
