World-Model Data Collection Architect

This Is Growth; San Francisco (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

Unknown AI lab in San Francisco is seeking a data collection operations lead to own end-to-end programs for multimodal data, including egocentric video, robotics, and sensors. You will translate researcher requests into scoped specs, design schemas, and set quality bars that teams can trust in deployment.

You will build QA infrastructure, run vendor management, capacity planning, and weekly reviews with researchers and engineers. This is a hands-on builder role in a fast-moving lab environment.

Qualifications

• Hands-on experience running physical-world data collection operations (egocentric video, robotics, AV data).
• Fluency in the model development lifecycle from data perspective (pre-training to eval).
• Ability to define taxonomies and quality criteria with researchers and engineers.
• Strong experience building QA systems, schemas, and data delivery SLAs.
• Willingness to work in the Bay Area or relocate.

Responsibilities

• Run field and lab data collection programs across multimodal data.
• Turn researcher requests into scoped specs with clear quality criteria.
• Maintain QA machinery with schema/timestamp/frame-drop checks on data.
• Manage collection vendors and capacity planning.

Skills

Data collection ops

Model lifecycle data

Cross-functional collaboration

Operational dashboards

Hands-on ownership

Relocation ready

XR/AR data

Schema ownership

Startup experience

Tools

QA dashboards

Job description

Unknown AI lab in San Francisco is seeking a data collection operations lead to own end-to-end programs for multimodal data, including egocentric video, robotics, and sensors. You will translate researcher requests into scoped specs, design schemas, and set quality bars that teams can trust in deployment.

You will build QA infrastructure, run vendor management, capacity planning, and weekly reviews with researchers and engineers. This is a hands-on builder role in a fast-moving lab environment.