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Human Archive

Provides real-world multimodal data for robotics

Head of Engineering Operations

DevOps & InfrastructureConfirmed live in the last 24 hours
Full-timeIndiaNo salary listedIn-person

About the Job

Requirements

  • A degree in engineering or computer science, or equivalent hands-on experience.
  • Comfort with Linux, including mounting and formatting media, filesystems, permissions, scheduled jobs, and reading logs.
  • Working Python skills sufficient to script batch jobs, walk directory trees, and call APIs.
  • Fluent use of AI tools such as Claude, Claude Code, Cowork, or equivalent for scripting, failure investigation, and procedure drafting, with the ability to verify their output.
  • Hardware troubleshooting ability based on forming hypotheses, testing them, and eliminating causes systematically.
  • Care with data integrity and chain of custody, including discipline in following verification steps.
  • Clear written English for writing procedures used by other people.
  • Willingness to work with physical equipment and travel to sites.
  • Comfort defining processes in an environment where processes may not yet exist.

Responsibilities

  • Build, configure, maintain, and reliably replicate ingest stations across locations, including hardware, operating systems, and tooling.
  • Own field ingest tooling by deploying updates, fixing failures, and carrying requirements back to the software team.
  • Define verification and media lifecycle rules for the operations team.
  • Investigate failed and partial ingests, identify root causes across media, devices, stations, tooling, and procedures, and fix underlying issues.
  • Audit process adherence and close identified gaps.
  • Maintain inventory and chain of custody for capture devices, memory cards, and drives across locations.
  • Reconcile inventory and investigate discrepancies to their root causes.
  • Specify and evaluate hardware, including cards, readers, hubs, enclosures, and drives, and manage supplier returns and warranty claims.
  • Plan capacity, card and drive rotation, and operating margin.
  • Investigate recording failures through controlled tests and isolate whether faults are in devices, media, stations, or procedures.
  • Decide whether equipment should be escalated, repaired, or retired, and document the supporting evidence.
  • Define capture configuration standards and verify device settings in the field.
  • Serve as the technical escalation point for the operations lead, quality control team, and field operators.
  • Train operators on tooling and hardware and write procedures that new joiners can follow independently.
  • Travel to deployment sites as needed.
  • Produce recording yield, data loss, and operator performance reports, and distinguish real failures from measurement artifacts.
  • Write Python and shell scripts to remove manual workflow steps.
  • Work with the software team on tooling requirements and file precise bug reports.

Desired Qualifications

  • Experience with cameras or sensor hardware.
  • SQL or experience querying an internal API for reporting.
  • Experience administering storage systems such as RAID, ZFS, or network-attached storage.
  • Prior work in data collection, field operations, logistics, or laboratory operations.
  • Experience with barcode or asset-tracking systems.

About the Company

Human Archive

Provides real-world multimodal data for robotics

Human Archive collects and curates real-world, multimodal data to train physical AI and robotics systems. It uses a hardware-enabled platform with camera-equipped headsets, tactile gloves, and motion-capture systems to record and synchronize egocentric video, depth, audio, motion, and haptic logs across homes, restaurants, and industrial settings. The data undergoes quality assurance, anonymization, and annotation to produce high-fidelity datasets for robotics companies and AI research labs. By providing large-scale data collection and labeling infrastructure through its partner network, the company aims to address the shortage of real-world training data for embodied intelligence and physical AI models.

Company size
51-200
Company stage
Seed
Total funding
$8.3M
Headquarters
San Francisco, CA
Founded
2025

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