VP of Physical AI & Autonomous Systems

Reports to: CEO
Team Scope: AI/ML Controls Data Engineering Simulation Embedded Systems

Mission

Build Robozes Physical AI layer transforming our machines from advanced manufacturing systems into self-optimizing autonomous production platforms.

This role is responsible for creating:

• Autonomous process intelligence inside Roboze machines
• AI-powered factory optimization for customers
• A long-term proprietary data ecosystem across materials parameters and qualification workflows

This is not a software AI role.
This is AI applied to physics materials and real-world production systems.

Strategic Mandate

1-Autonomous Process Intelligence (Machine-Level AI)

Make Roboze systems self-learning and self-optimizing.

• Develop AI models that optimize process parameters (temperature pressure speed cooling curves etc.)
• Real-time defect detection and closed-loop correction using in-situ monitoring and dynamic process parameter adjustment.
• Adaptive parameter tuning for new geometries and materials
• Reduce operator dependency
• Increase first-time-right rate
• Improve gross margins through yield optimization
• Implement assisted algorithms for converting metal part designs into additive composite-ready build files

Goal: Every Roboze machine improves over time. Every Roboze machine autonomously determines how to produce each part. Create Robozes proprietary Process Intelligence Operating System (PIOS)

2-AI for Factory-Level Optimization (Customer Layer)

Extend intelligence beyond the machine:

• Predictive maintenance models
• Production scheduling optimization
• Scrap reduction AI
• Qualification acceleration tools
• AI-based digital twins for simulation before and during printing
• Connect Roboze machines with AGVs and robotic solutions to orchestrate end-to-end automated factory workflows enabling 24/7 autonomous production.

Goal: Create Robozes proprietary Factory Intelligence Operating System (FIOS)

3-Build the Roboze Data Ecosystem

• Architect centralized data infrastructure across:

  • Machine sensor data
  • Material behavior data
  • Qualification workflows
  • Failure modes

• Develop proprietary datasets

• Protect and structure process knowledge as a defensible asset

• Collaborate with materials and qualification teams

Goal: Create Robozes proprietary Data Intelligence Operating System (DIOS)

What Success Looks Like (2436 Months)

• Autonomous parameter optimization live on all new systems
• 1525% yield improvement via AI
• Reduced sales cycle via AI-driven qualification tools
• Recurring AI software revenue layer
• Proprietary dataset unmatched in high-performance polymer AM

Key Responsibilities

• Define and execute Robozes Physical AI roadmap
• Build and lead cross-functional AI team (ML Controls Embedded Data)
• Partner with Materials Hardware and Applications teams
• Drive AI monetization strategy
• Establish long-term architecture (edge cloud hybrid)
• Oversee AI governance IP protection and data strategy

Ideal Profile

Background

• 10 years in AI/ML applied to physical systems

• Experience in robotics aerospace systems semiconductor manufacturing or advanced industrial automation

• Deep understanding of:

  • Control systems
  • Sensor fusion
  • Physics-informed ML
  • Real-time optimization

Strongly Preferred

• Experience with:

  • Industrial robotics
  • Semiconductor fabs Aerospace & Defense fabs.
  • Additive manufacturing
  • High-performance materials

• Track record of shipping production AI systems (not research only)

• Built AI products with measurable ROI

Technical Stack Exposure (Desired)

• ML frameworks: PyTorch TensorFlow
• Edge AI deployment
• Reinforcement learning
• Bayesian optimization
• Digital twins / simulation modeling
• Time-series data systems
• Cloud infrastructure (AWS/GCP/Azure)
• Real-time systems integration

Leadership Expectations

• Think like a platform architect not a feature builder
• Balance speed and scientific rigor
• Translate physics problems into data problems
• Build long-term defensibility not short-term demos
• Operate with founder-level ownership and speed. Cut through bureaucracy. Question default assumptions and build new standards.

Why This Role Matters

Robozes future is not just machines.
It is: Materials Qualification Physical AI

This role is responsible for making Roboze:

• Harder to copy
• Faster to deploy
• More profitable
• Increasingly autonomous