VP of AI
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Job Description
VP of AI leading AI architecture and organization at 4MP for autonomous precision manufacturing. Collaborating with founding leadership to create intelligence layers for manufacturing systems.
Responsibilities:
- Build and Manage the AI Organization: You will build and manage the AI organization at 4MP, while doing hands-on technical work. You will recruit and mentor a world-class team while architecting the AI modules, writing code, reviewing architecture, and building prototypes alongside them.
- Lead AI Architecture: Design and develop the intelligence layers that enable machines to interpret manufacturing intent, understand system deviations, and autonomously generate corrective actions.
- Integrate Multidisciplinary Data: Define how AI interacts with physics-based models, machine telemetry, sensor data, CNC programs, and manufacturing context.
- Interpret & Modify CNC Programs: Build AI systems to extract machining intent from G-code, accounting for operator variability, CAM differences, and varied programming styles.
- Design Learning Frameworks: Develop feedback loops that combine AI reasoning with physics-based models and sensor data, allowing machines to improve from each correction cycle and production result.
- Build Data Infrastructure: Create a scalable data architecture that captures manufacturing intelligence across machines, parts, tools, materials, and production environments.
- Create Simulation Environments: Define simulation and validation environments for training, testing, and scaling autonomous correction models.
- Drive Strategic Cross-Functional Leadership: In close interaction with other functional leaders, you will contribute to defining the company strategy, technology and business roadmaps, and comprehensive project plans.
Requirements:
- Hands-On AI Management: Proven experience building and managing an AI organization while doing hands-on technical work. You must be comfortable leading teams while actively contributing to the codebase.
- 10+ Years of Experience: Deep background in AI, software engineering, machine learning architecture, robotics, intelligent systems, or complex technical platforms.
- Industrial Domain Expertise: Direct experience with robotics, autonomous systems, industrial systems, physics-informed machine learning, reinforcement learning, control systems, simulation, digital twins, or real-world telemetry.
- System Architecture Expertise: Deep experience designing and building production-grade AI, ML, data, or software infrastructure from the ground up.
- Strong Coding and Data Proficiency: Exceptional hands-on coding skills and ability to manage data pipelines, model orchestration, validation, feedback loops, and complex, messy, real-world data.
- Core AI/ML Expertise: Strong, up-to-date understanding of optimization, statistical modeling, and system-level AI design.
- Educational Foundation: Bachelor’s degree in Computer Science, Robotics, Engineering, Physics, Mathematics, or a related field.
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