Operations Manager, Data Aquisition

OpenAI•San Francisco, CA

About The Position

We are seeking a Leader, DAQ Operations to own and scale the system that enables robotics data collection across OpenAI’s robotics program. This system spans our robotic stations, a large managed workforce, and facilities, while engineering and research teams continuously evolve the platforms and workflows running on top. You will sit at the center of this system, ensuring alignment across research, engineering, operations, and external partners so data collection runs reliably at scale. This is a single-threaded ownership role—responsible for making the system operate cohesively, scale across sites, and continuously improve as robotics capabilities evolve. Success in this role requires strong cross-domain leadership and coordination across senior stakeholders, including robotics engineering, software teams, research leaders, operations teams, and external service partners.

Requirements

Have led complex, cross-functional systems spanning engineering, operations, and external partners
Operate as a single-threaded owner who drives alignment and execution across ambiguous environments
Can impose structure, operating cadence, and accountability on messy, evolving systems
Are equally comfortable with senior technical stakeholders and frontline operational teams
Default to action, speed, and clarity over process for its own sake
Enjoy owning system-level outcomes, not just functional pieces

Responsibilities

Own the DAQ Operating System: Own the end-to-end system that enables robotics data collection across stations, workforce, and facilities
Ensure robotics fleet, station software, workforce execution, and infrastructure operate as a single, reliable system
Set operating rhythms, decision frameworks, and escalation paths that keep execution unblocked
Drive step-function improvements in throughput, reliability, and data quality
Drive Cross-Functional Execution: Act as the single point of operational alignment across robotics research, engineering, and operations
Force clarity on priorities, readiness, and constraints to keep deployments moving
Ensure new platforms, workflows, and capabilities land cleanly into operations
Push real-world operational feedback upstream to shape engineering and research decisions
Own Workforce & Vendor Performance: Own execution of the managed workforce model through a services partner
Set expectations, metrics, and escalation paths with vendor leadership
Ensure workforce readiness keeps pace with changing station workflows and programs
Hold partners accountable to performance, throughput, and quality outcomes
Run Fleet, Software, and Infrastructure Coordination: Ensure tight coordination across fleet reliability, station software, and operational infrastructure
Own readiness of materials, consumables, and station inputs required for continuous execution
Surface issues early, drive fast resolution, and prevent recurrence through system fixes
Scale Site Operations: Own expansion of DAQ operations
Define and enforce repeatable operating standards across sites
Build site leadership structure and coordination mechanisms that scale with growth
Ensure consistent execution as complexity and footprint increase
Drive Alignment in Ambiguous Environments: Create clarity across senior stakeholders when priorities, timelines, and constraints conflict
Make tradeoffs explicit and drive decisions to keep execution moving
Maintain tight communication across engineering, research, and operations as systems evolve