Technical Program Manager, Robotics Data Acquisition

About Team Our Robotics team is focused on unlocking general-purpose robotics and advancing toward AGI-level intelligence in dynamic, real-world environments. Working across the full model and systems stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the physical constraints of real-world systems to improve people’s lives. About the Role We’re looking for a Technical Program Manager to own and scale the systems that power robotic data acquisition across our development and evaluation environments. This role sits at the intersection of Robotics Engineering, Operations, and Infrastructure, ensuring that DAQ stations and associated workflows reliably produce high-quality data for model training and evaluation. You will drive end-to-end execution of complex, cross-functional programs that integrate robotic platforms, sensing systems, operator tooling, and data pipelines into cohesive, production-ready systems. Success in this role requires strong systems thinking, operational rigor, and the ability to translate ambiguous research needs into scalable infrastructure. In this role you will: - Own DAQ Program Delivery Lead the roadmap, execution, and scaling of robotic DAQ systems, ensuring alignment with research, engineering, and operational priorities. - Drive Cross-Functional Integration Coordinate across robotics hardware, software, infrastructure, and operations teams to deliver tightly integrated, deployment-ready data collection systems. - Operationalize Data Collection Systems Translate experimental and research workflows into repeatable, scalable DAQ processes with clear SLAs, metrics, and reliability targets. - System Readiness & Deployment Ensure DAQ stations (robots, sensors, compute, operator interfaces) are fully integrated, validated, and ready for production use across multiple sites. - Program Execution & Risk Management Build and manage detailed program plans, identify risks early, and drive mitigation across technical and operational domains. - Capacity & Throughput Planning Model and plan DAQ capacity (stations, operators, uptime) to meet evolving data demands, balancing speed, cost, and quality. - Quality & Data Integrity Oversight Partner with engineering and data teams to define and enforce data quality standards, ensuring consistency and usability for downstream model training. - Continuous Improvement Drive improvements in system reliability, utilization, and efficiency through instrumentation, feedback loops, and process optimization. You might thrive in this role if you: - 5+ years of experience in technical program management, systems engineering, or operations in hardware, robotics, or infrastructure-heavy environments - Strong systems-level thinking across hardware, software, and operational workflows - Proven ability to lead complex, cross-functional programs with high ambiguity - Experience scaling physical systems (labs, factories, robotics fleets, or test infrastructure) - Comfortable working close to hardware and debugging real-world system issues - Strong analytical skills; able to model capacity, throughput, and system performance - Excellent communication skills with the ability to align diverse stakeholders About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (includin

5+ years of experience in technical program management, systems engineering, or operations in hardware, robotics, or infrastructure-heavy environments. Strong systems-level thinking across hardware, software, and operational workflows. Proven ability to lead complex, cross-functional programs with high ambiguity. Experience scaling physical systems (labs, factories, robotics fleets, or test infrastructure). Comfortable working close to hardware and debugging real-world system issues. Strong analytical skills; able to model capacity, throughput, and system performance. Excellent communication skills with the ability to align diverse stakeholders.

Own DAQ Program Delivery: Lead the roadmap, execution, and scaling of robotic DAQ systems, ensuring alignment with research, engineering, and operational priorities. Drive Cross-Functional Integration: Coordinate across robotics hardware, software, infrastructure, and operations teams to deliver tightly integrated, deployment-ready data collection systems. Operationalize Data Collection Systems: Translate experimental and research workflows into repeatable, scalable DAQ processes with clear SLAs, metrics, and reliability targets. System Readiness & Deployment: Ensure DAQ stations (robots, sensors, compute, operator interfaces) are fully integrated, validated, and ready for production use across multiple sites. Program Execution & Risk Management: Build and manage detailed program plans, identify risks early, and drive mitigation across technical and operational domains. Capacity & Throughput Planning: Model and plan DAQ capacity (stations, operators, uptime) to meet evolving data demands, balancing speed, cost, and quality. Quality & Data Integrity Oversight: Partner with engineering and data teams to define and enforce data quality standards, ensuring consistency and usability for downstream model training. Continuous Improvement: Drive improvements in system reliability, utilization, and efficiency through instrumentation, feedback loops, and process optimization.