Sr Manager, Data Factory Operation

Company Overview

Faraday Future (FF) is a California-based embodied artificial intelligence ecosystem company, leveraging the latest technologies and the world's best talent to realize exciting new possibilities in mobility and robotics. We produce user-centric, technology-first vehicles and robots to establish new paradigms in human-AI interaction and aim to change the way we drive and interact with machines.

As the Senior Manager of Data Factory Operation, you will lead the mid-end functions of Data Factory, overseeing both centralized and decentralized data production pipelines. You are responsible for the end-to-end execution of large-scale data collection—from environment design to real-world robot data submission—to support the training of Embodied AI (EAI) models.

• On-demand Environment Construction:
  Direct the design, renovation, and construction of physical data collection sites based on specific project needs.
• Scenario Layout:
  Manage the setup of diverse scenarios, including retail (supermarket shelves), light manufacturing (electronics assembly), and industrial production lines (conveyor belts).
• Resource Management:
  Oversee the rental and maintenance of centralized collection robots and data processing hardware.
• Workforce Management:
  Lead the global contributor network, including onboarding, contracting, and registration of decentralized data collectors.
• Throughput Management:
  Execute high-volume collection tasks, such as the 20,000-hour industrial luggage factory requirement, managing 50+ devices across multiple shifts.
• SOP Development:
  Maintain and update the "Data Collector User Manual," covering VR teleoperation instructions and sensor calibration protocols.
• Standardization:
  Design and enforce action standards and acceptance criteria for all collected data.
• Data Integrity:
  Ensure technical compliance (1080p+, 30
  
  FPS+, FOV 130+, IMU sync) and reject unstable or occluded footage.
• Preliminary Acceptance:
  Supervise the screening, filtering, and clipping of raw robot data and egocentric streams before submission to clients.
• Compliance & Privacy:
  Guarantee that all visible PII is anonymized/blurred before delivery.
• Remote & On-site Support:
  Provide technical and operational support for client-specific teleoperation and data inquiry needs.
• Feedback Loops:
  Continuously optimize data collection actions and execution protocols based on client feedback and model performance metrics.

• Bachelor’s or Master’s degree in Robotics, Computer Science, Mechanical Engineering, Industrial Engineering, Data Science, or a related technical field.
• 10+ years of experience in large-scale operations, robotics data collection, AI/ML data operations, manufacturing operations, operations management or aftersales.
• Deep understanding of data privacy laws (GDPR/CCPA) and PII handling requirements.
• Proven experience managing complex data production pipelines across centralized and distributed operational models.
• Strong understanding of Embodied AI (EAI), robotics systems, teleoperation workflows, sensor systems, and multimodal data collection processes.
• Hands-on experience designing and operating physical collection environments such as retail spaces, warehouses, manufacturing lines, or industrial simulation environments.
• Demonstrated ability to scale high-throughput operations involving large device fleets, multi-shift execution, and geographically distributed contributors.
• Experience developing and enforcing SOPs, operational standards, quality control processes, and acceptance criteria for large-scale data operations.
• Familiarity with robotics hardware and sensors, including RGB/RGB-D cameras, IMUs, LiDAR, VR teleoperation systems, and edge compute devices.
• Strong knowledge of data quality requirements, including video resolution standards, synchronization validation, calibration procedures, and dataset integrity checks.
• Experience with data annotation, preprocessing, clipping, filtering, and dataset acceptance workflows for AI/ML training pipelines.
• Understanding of privacy, compliance, and data governance requirements, including anonymization and handling of PII.
• Proven ability to manage…