Job Description
Roboze is redefining how mission-critical parts are made, locally, on demand, and without compromise. Our advanced materials and additive manufacturing platforms are trusted by top Aerospace & Defense organizations around the world. From Europe’s leading primes to major U.S. defense contractors, we help engineers move faster, build stronger, and achieve performance that traditional manufacturing cannot match. We are not following the old rules of manufacturing. We are rewriting them. And now, from our growing hub in El Segundo, California, we are expanding the team. Why This Role Matters Build Roboze’s 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. Reports to: CEO Team Scope: AI/ML, Controls, Data Engineering, Simulation, Embedded System Strategic Mandate 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 Roboze’s proprietary Process Intelligence Operating System (PIOS) 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 Roboze’s proprietary Factory Intelligence Operating System (FIOS) Build the Roboze Data Ecosystem Architect centralized data infrastructure across: Machine sensor data, Material behavior data, Qualification workflows and Failure modes Develop proprietary datasets Protect and structure process knowledge as a defensible asset Collaborate with materials and qualification teams Goal: Create Roboze’s proprietary Data Intelligence Operating System (DIOS) What Success Looks Like (24–36 Months) Autonomous parameter optimization live on all new systems 15–25% 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 Roboze’s 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 Who You Are · A strong communicator who enjoys working directly with customers and guiding complex technical discussions. · A strategic thinker with a hands-on engineering mindset, capable of turning customer challenges into real applications. · Comfortable working cross-functionally with Business Development, Sales, Marketing, and Production teams. · Proactive, organized, and autonomous in managing tasks and priorities. · Experienced in, or motivated to grow within, Aerospace, Defense, or advanced materials environments. What We Are Looking For 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 and Real-time optimization