Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- Certification in 5S or Six Sigma.
- 7 years of experience in production management, set design/staging, or factory/lab floor management.
- 6 years of experience in tracking multi-stream workflows, action items, inventory, and safety compliance.
- 6 years of experience in research and development (R&D) environments, bridging creative staging with technical engineering.
Preferred qualifications:- Experience in formula 1 style hardware operations environment or startup.
- Experience working across hardware and software systems, including identifying mechanical failures and system-level issues.
About the jobArtificial intelligence will be one of humanity's most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer various learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
US: $165,000 - $239,000 (USD) 20% bonus target equity benefits.
Responsibilities- Architect and execute scene staging for robotic data collection, focusing on set design and deployment, applying high-end production standards to robotic integration.
- Act as the primary liaison between researchers defining data goals, engineering teams integrating robotic work cells, and production teams managing staging logistics.
- Coordinate scene layouts with robot work cell configurations, ensuring environments are optimized for high-quality data capture while maintaining safe, efficient robotic operations.
- Manage inventory, scheduling, and logistics for all set components, props, and equipment.
- Monitor scene performance and data quality to pinpoint friction points, identifying bottlenecks in set design or integration and implement iterative, systemic improvements.
