---
title: 'Head of Engineering Operations at Human Archive'
canonical: 'https://feeny.ai/job/head-of-engineering-operations-human-archive-india-19b8vd5cprjr'
type: 'job'
last_seen: '2026-09-16'
---

# Head of Engineering Operations at Human Archive

- **Company:** Human Archive
- **Location:** India
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-14
- **Last confirmed live:** 2026-09-16
- **Apply:** https://jobs.ashbyhq.com/humanarchive/9c39baff-70f6-4a7a-94c2-218f04d5bc25

## Job description

## 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

## About Human Archive

## Core Business
- **Primary industry**: Data infrastructure for robotics and embodied artificial intelligence
- **Target customers**: Frontier AI labs, robotics companies, and academic researchers (B2B)
- **Mission**: To build the “Common Crawl for human sensorimotor intelligence” – a massive, aligned multimodal dataset that captures how humans interact with the physical world, enabling robots to automate manual labor and advancing understanding of human cognition.

## Products & Services
- **HA-Multi Dataset**: Fully aligned multimodal dataset including RGB-D (stereo depth via IR dot projection), tactile gloves, body IMUs, wrist cameras, and audio. Provides structured outputs such as 3D MANO hand reconstructions, 2D tactile force maps, depth maps per timestamp, and human pose reconstructions.
- **HA-Ego Dataset**: Monocular RGB vision dataset with wrist cameras, annotated with environment/scene descriptions, task labels, hand tracking, object segmentation, SLAM, and 3D pose reconstruction.
- **Custom Hardware Rigs**: Proprietary headset-mounted cameras, tactile gloves, full-body motion capture suits, and wrist cameras designed for synchronized data capture across multiple modalities.
- **Data Collection & Annotation Services**: End-to-end pipeline including QA, anonymization (face blurring), and annotation, delivered as structured datasets to customers.
- **Model Fine‑Tuning & Evaluation**: Internal capability to fine‑tune AI models on collected data and test them on robots to validate dataset quality.

## Market Standing
- **Valuation / Market Cap**: Not disclosed (seed‑stage private company)
- **Key Metric**: Total funding raised – $8.2 million (seed round, announced May 2026)
- **Notable Investors/Partners**: Wing Venture Capital, NVP Capital, Y Combinator, and angels from OpenAI, Nvidia, Google, Mercor, AfterQuery, BAIR, SAIL, Brad Boa, and Meta. [techcrunch.com](https://techcrunch.com/2026/05/26/human-archive-taps-into-indias-services-startups-to-collect-data-for-physical-ai/)
- **Growth Signals**:
  - 1,000+ active headsets deployed across multiple locations (homes, hotels, restaurants, etc.)
  - Capacity to collect up to 8,000 hours of data per day
  - Signed national‑level partnerships to scale contributor network to 50,000+ people
  - Expanding operations from India into Southeast Asia and the United States
  - Already shipped datasets to frontier research teams

## Competitive Advantages
- **First‑mover in synchronized multimodal data at scale**: No other company has been able to simultaneously capture headset RGB‑D, force feedback, full‑body motion capture, and wrist camera data in a time‑aligned manner across thousands of real‑world environments.
- **Custom hardware**: Over seven proprietary hardware products (rigs, caps, tactile gloves, suits) designed specifically for high‑quality, scalable data collection.
- **On‑the‑ground operations in India**: Low‑cost, high‑volume data collection through partnerships with gig‑economy service providers, allowing rapid scaling.
- **Strong founding team**: Researchers from Stanford and Berkeley with deep backgrounds in robotics, hardware, and operations.

## Strategic Focus
- **Scaling data collection**: Expanding the contributor network to 50,000+ people and entering new geographies (Southeast Asia, U.S.).
- **Building the “Common Crawl for human sensorimotor intelligence”**: Creating the largest annotated multimodal dataset of human physical interaction.
- **Vertical integration**: Developing fine‑tuning and model evaluation services to demonstrate dataset quality and capture more value.
- **Partnerships with service companies**: Offering discounted cleaning, cooking, or other services in exchange for data collection consent (early pilot stage).

## Why Work Here
- **Mission‑driven**: Opportunity to work on foundational infrastructure for automating manual labor and advancing embodied AI – a historic inflection point.
- **Early‑stage impact**: Join a Y Combinator‑backed startup just months old, with direct influence on product, data pipeline, and research direction.
- **Work environment**: Hybrid/remote options available; offices in San Francisco (in‑office for some roles, remote for others). [builtin.com](https://builtin.com/company/human-archive)
- **Team culture**: Small, tight‑knit team (18 employees as of mid‑2026) of engineers and operators who “dropped out of Stanford and Berkeley” to pursue this vision.
- **Cutting‑edge tech**: Work with custom hardware, multimodal sensor fusion, large‑scale data pipelines, and state‑of‑the‑art robotics models.
- **Open roles**: Engineering (Research, ML, Infrastructure, Firmware, Embedded, Hardware Test) and Operations (Head of Operations). [ycombinator.com](https://www.ycombinator.com/companies/human-archive/jobs)

## Sources
1. [humanarchive.ai](https://www.humanarchive.ai/) – Company website
2. [builtin.com](https://builtin.com/company/human-archive) – Company profile, headcount, office locations, job listings
3. [ycombinator.com](https://www.ycombinator.com/companies/human-archive) – Y Combinator company page, founding details, funding, jobs
4. [techcrunch.com](https://techcrunch.com/2026/05/26/human-archive-taps-into-indias-services-startups-to-collect-data-for-physical-ai/) – TechCrunch article on $8.2M seed round, business model, and operations

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