Robotics Engineer: Process

Summary

Headquartered in sunny Los Angeles, Gray Matter Robotics is a well‑capitalized AI robotics startup serving the manufacturing industry. We empower shop floor workers with our smart robots that assist with tedious and ergonomically challenging tasks, specifically in automated surface finishing. Our proprietary GMR‑AI™ software is integrated with state‑of‑the‑art industrial robots, sensors, and tools to create application‑specific turnkey solutions for customers through a Robot‑as‑a‑Service (RaaS) model.

At Gray Matter Robotics, we build from the ground up, including how we architect our teams. We don’t hire for generic titles; we break our business needs down to their fundamental truths. Driven by first principles, we deconstructed our goals to see what we truly needed and as a result, we engineered this role specifically to bridge the following gap:

GMR’s robots handle some of the most demanding surface finishing applications in manufacturing — sanding, polishing, coating removal — across customers like Caterpillar, Wabash, HII, and Pierce. The physics of material removal are complex, non‑obvious, and deeply variable across parts and environments. We don’t just deploy robots; we build the quantitative process science that makes them work. The Process team generates that science: designing experiments, characterizing materials, building predictive models, and translating R&D into deployable, repeatable automation.

We need a process engineer who thrives in exactly this kind of environment — someone who can move between ambiguous R&D problems and live customer deployments without missing a beat.

• Develop and optimize robotic manufacturing processes for surface finishing applications including sanding, polishing, spray coating, and coating removal.
• Design and execute structured experiments (DoE methodology) to characterize material removal physics and quantify process parameters.
• Build and refine process models that translate experimental findings into deployable, customer‑ready automation.
• Apply material science knowledge to understand substrate behavior at the chemical and mechanical level and use that understanding to inform process decisions.
• Collaborate with customers to understand their specific requirements and translate them into process specifications.
• Support the design and implementation of new end‑of‑arm tooling (EOAT) for robotic manufacturing processes.
• Analyze manufacturing data to identify patterns, validate models, and drive continuous process improvement.
• Work cross‑functionally with robotics, systems, and application engineers to integrate process improvements into customer deployments.
• Leverage AI and data analysis tools (Python, statistical software) to accelerate process characterization, identify parameter interactions, and build more accurate predictive models.
• Travel to customer sites as needed, up to 50% of the time.

In order to comply with the export regulations of the United States Government, it is required that the applicant must fall under one of the following categories: (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (i.e. current green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158.

• Bachelor’s degree or higher in Mechanical Engineering, Robotics, Material Science, or a closely related field.
• 2+ years of industry experience in manufacturing process development, process engineering, or a directly related role.
• Demonstrated use of Design of Experiments (DoE) or Design for Six Sigma (DFSS) methodology in daily work. Qualifying experience must come from at least one of the following: (i) testing at school or in the workplace, (ii) design or process optimization, (iii) factory or production optimization, or (iv) reliability or quality testing. Certification is not required; practical application is.
• Proficiency in material science principles and their application to manufacturing processes.
• Strong analytical and problem‑solving skills; comfortable working from data to conclusions.
• Experience working in a research capacity, in…