Robotics Engineer: Process

About this role

Summary

GrayMatter Robotics is a well-capitalized Physical AI company headquartered in Los Angeles, building Factory SuperIntelligence (FSI) through systems that learn, adapt, and develop genuine process intelligence across every part they touch. Our robots take on the tedious, hazardous, and ergonomically demanding work that has kept manufacturing dependent on a shrinking skilled workforce, delivering superhuman performance. Robotics has a demo problem. GMR doesn't. Our systems are in active production today, adapting autonomously to real geometries, real materials, and real process variation without reprogramming. We deliver our system through a Robots-as-a-Service (RaaS) model that converts capital expenditure into an operating subscription, making Physical AI accessible to the manufacturers who need it most. We hire people who want to solve hard physical problems at an industrial scale.

The Problem Statement

At GrayMatter 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.

Role & Responsibilities

- 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.

Export Control Requirements

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.

Minimum Qualifications

- 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 either an academic or industry setting.

- Willingness to travel to customer sites as required by the role.

- Ability to operate effectively in a fast-paced environment with rapidly changing priorities.

- Must meet export control requirements in the United States and maintain ongoing work authorization beginning at the time of hire through the entirety of employment.

Preferred Qualifications

- Master’s degree or higher in Mechanical Engineering, Robotics, Material Science, or a closely related field.

- 5+ years of experience in process engineering within a manufacturing, robotics, or automation environment.

- Design for Six Sigma (DFSS) certification or equivalent structured experimental design training.

- Experience with robotic systems and end-of-arm tooling, particularly in surface finishing or material removal applications.

- Proficiency in Python or C++ for data analysis, statistical modeling, or process automation.

- Experience with CAD software (SolidWorks, NX, CATIA) for EOAT collaboration and design review.

- Background in both R&D and commercial production environments; comfort switching between early-stage development and structured customer deployments.

- Demonstrated use of AI or machine learning tools to accelerate process characterization, predict parameter interactions, or optimize exp

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