Physical AI Senior Manager

Senior Manager role focused on Physical AI strategy, advisory, and implementation in manufacturing and supply chain. Drives transformation programs combining robotics, computer vision, simulation, digital twins, synthetic data, and edge AI. Owns solution shaping, architecture, and end-to-end delivery from data to operations, with a focus on measurable outcomes and integrating AI with real-world constraints.

What you'd actually do

  1. Lead Physical AI strategy and advisory for manufacturing and supply chain clients. Identify high-value use cases (e.g., quality inspection, safety, intralogistics, material handling, asset monitoring, autonomous operations), define value hypotheses, and translate to roadmaps and business cases.
  2. Own solution shaping and end-to-end architecture spanning sensors, vision, data pipelines, model development, simulation, edge deployment, and operations (i.e., MLOps and ModelOps), with explicit acceptance criteria for operational environments.
  3. Drive PoCs and pilots to measurable outcomes. Define experiments, data collection plans, synthetic data approaches (when appropriate), evaluation metrics, and scale plans from pilot-to-plant and factory/network rollout.
  4. Integrate AI with real-world constraints: latency, reliability, safety, OT/IT connectivity, cybersecurity, model drift, human-in-the-loop workflows, and maintenance/operating model considerations.
  5. Lead and mentor multi-disciplinary teams: data science, ML engineering, software and edge, vision, robotics and controls, manufacturing experts, and contribute to capability-building and market activation.

Skills

Required

  • Client leadership
  • team leadership
  • business development/pursuits
  • Manufacturing and supply chain domain experience
  • robotics
  • computer vision
  • simulation
  • digital twins
  • synthetic data
  • edge AI
  • MLOps
  • ModelOps
  • strategy development
  • advisory
  • architecture design
  • proof of concept (PoC) leadership
  • pilot execution
  • data collection planning
  • evaluation metrics definition
  • scaling plans
  • integration with real-world constraints (latency, reliability, safety, OT/IT connectivity, cybersecurity, model drift, human-in-the-loop, maintenance)
  • collaboration with alliance partners
  • technical authority
  • mentoring multi-disciplinary teams

Nice to have

  • controls engineers
  • data scientists
  • frontline operations translation
  • NVIDIA
  • Siemens
  • AWS

What the JD emphasized

  • Physical AI
  • manufacturing and supply chain
  • robotics
  • computer vision
  • simulation
  • digital twins
  • synthetic data
  • edge AI
  • MLOps
  • ModelOps
  • measurable outcomes
  • real-world constraints
  • latency
  • reliability
  • safety
  • OT/IT connectivity
  • cybersecurity
  • model drift
  • human-in-the-loop workflows
  • maintenance/operating model considerations

Other signals

  • Physical AI
  • robotics
  • computer vision
  • simulation
  • digital twins
  • edge AI
  • MLOps
  • ModelOps
  • synthetic data