Strategy & Project Lead (Data Operations) at Human Archive (India)

Human Archive· India·1d ago

Role details

Employment

Full-Time

Job description

ABOUT THE ROLE

Human Archive (YC W26) is hiring someone to lead our end-to-end data annotation operations.

You will work directly with the founder to build and manage a high-performing annotator workforce for robotics data. Your job is to ensure we consistently have the right people, quality, and throughput across multiple projects running simultaneously.

This is a highly hands-on role for someone who is excellent with people, metrics, and fast-moving operations.

WHAT YOU WILL DO

Recruit, evaluate, and manage high-quality data annotators
Maintain a high bar for annotation quality and workforce performance
Allocate annotators across multiple simultaneous projects
Track daily throughput, quality, attendance, utilisation, and project targets
Build dashboards and operating systems with our engineering team
Identify bottlenecks and improve annotation workflows
Design incentives that help teams consistently meet daily targets
Forecast staffing requirements for upcoming projects
Manage team leads and resolve day-to-day operational issues
Build processes that can scale from hundreds to thousands of annotators

WHO WE ARE LOOKING FOR

Extremely good with people and frontline teams
Strong analytical ability and comfort with metrics and dashboards
Excellent at prioritisation and resource allocation
Highly organised and able to manage multiple projects at once
Creative about incentives, hiring, performance, and operations
Clear, direct, and fast communicator
High ownership, energy, urgency, and agency
Comfortable working in a demanding, fast-changing startup environment

Experience at companies such as Surge AI, Scale AI, Mercor, Turing, Outlier, Remotasks, or similar workforce and data-operations businesses is a strong advantage.

We care more about your ability to execute than your degree, credentials, or years of experience.

LOCATION

On-site in Bangalore.

Why work at Human Archive

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
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

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
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).

Sources

humanarchive.ai – Company website
builtin.com – Company profile, headcount, office locations, job listings
ycombinator.com – Y Combinator company page, founding details, funding, jobs
techcrunch.com – TechCrunch article on $8.2M seed round, business model, and operations