Your mission
Design and scale manufacturing systems for AI-native materials discovery
Dunia is building a new kind of materials company, where discovery is driven by tightly coupled experimentation, automation, and AI. As our materials move from discovery toward scale, manufacturing systems must be designed not just for output, but for learning, reliability, and iteration.
As Manufacturing Systems Engineer, you will design and build the systems, processes, and flows that allow Dunia’s materials platforms to scale from lab-level validation to repeatable, industrially relevant production. You will think in systems: processes, equipment, data, controls, and people.
This role exists to ensure that scale does not break what makes Dunia so fast.
Dunia is building a new kind of materials company, where discovery is driven by tightly coupled experimentation, automation, and AI. As our materials move from discovery toward scale, manufacturing systems must be designed not just for output, but for learning, reliability, and iteration.
As Manufacturing Systems Engineer, you will design and build the systems, processes, and flows that allow Dunia’s materials platforms to scale from lab-level validation to repeatable, industrially relevant production. You will think in systems: processes, equipment, data, controls, and people.
This role exists to ensure that scale does not break what makes Dunia so fast.
Your tasks will include:
Design scalable manufacturing systems- Design end-to-end manufacturing processes for materials and devices transitioning out of R&D
- Define process flows, equipment requirements, and throughput models
- Ensure systems are robust, repeatable, and designed for iteration
- Work closely with research, automation, and AI teams to translate experimental workflows into scalable processes
- Identify where lab-scale assumptions break at higher throughput
- Ensure manufacturing constraints inform upstream discovery decisions
- Develop process documentation, control strategies, and quality frameworks
- Support facility layout, equipment integration, and commissioning
- Anticipate scale-up risks and address them early
- Design systems that generate high-quality process and performance data
- Enable feedback loops between manufacturing, experimentation, and AI models
- Treat manufacturing as a learning system, not a static endpoint