Senior Applied AI Engineer

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. You will join the Special Projects: AI team at GrayMatter Robotics, a cross-functional group responsible for designing, building, and operationalizing AI capabilities across GMR’s products and customer deployments. The team serves as a horizontal AI function, working across simulation, data, perception, inspection, learning-based control, process optimization, and system validation to enable robust autonomy for GMR’s robots and factories in high-mix manufacturing environments. The team operates end-to-end from problem formulation and data strategy to model development, simulation, deployment, and long-term performance monitoring collaborating closely with robotics software, solutions engineering, applications, process engineering, hardware, and deployment teams. The team’s mandate is to ensure that AI systems scale across processes, customers, and sites, and perform reliably on real robots in real factories, today, not just in controlled demonstrations. This role values strong technical depth and sound research judgment, but places a clear priority on system ownership, execution, and measurable business impact. Publishing and external technical contributions are encouraged when they naturally emerge from deployed systems while meaningfully advancing both GMR’s products and the broader robotics community; however, successful delivery, robustness, and customer-visible outcomes are the primary measures of success. KEY RESPONSIBILITIES - Design, implement, and train state-of-the-art AI models for perception, inspection, decision-making, and control in real-world robotic manufacturing systems. - Lead the development of simulation-based tooling used across a broad range of AI use cases at GMR, including reinforcement learning for recipe learning, scalable synthetic data generation, and autonomous robotic cell setup. - Own and advance synthetic data generation pipelines, leveraging generative AI techniques for complex 3D geometry and environment, physics-based simulation, and image data, and scaling them across diverse processes and applications. - Develop and deploy multi-modal inspection and health monitoring systems, integrating vision, 3D sensing, force/torque, and other sensor modalities. - Bridge the gap between simulation and reality, ensuring models trained in simulation transfer robustly to physical robotic cells. - Optimize, deploy, and maintain ML models on production robotic systems, considering latency, reliability, and hardware constraints. - Troubleshoot complex, cross-disciplinary issues spanning ML models, simulation environments, robotics software, sensors, and hardware. MINIMUM QUALIFICATIONS - Master’s Degree or PhD in Computer Science, Robotics, Mechanical Engineering or a closely related field plus 5-8 years of experience. - Strong proficiency in Python is required; candidates with strong working knowledge of both Python and C++ in robotics systems will be preferred. - Deep expertise in machine learning and deep learning, with hands-on experience using frameworks such as PyTorch. - Demonstrated experience working with real robotic manipulators, including deploying and testing machine learning models on physical robots operating in real-world environments. - Demonstrated experience working with simulation environments and/or physics-based modeling for robotics (e.g., Isaac Lab or MuJoCo). - Strong software engineering discipline, including writing clean, maintainable, well-tested, and performance-optimized code. - Proven ability to diagnose and solve ambiguous, system-level problems and iterate quickly under real-world constraints. PREFERRED QUALIFICATIONS - Experience with synthetic data generation and simulation-driven dataset creation for perception and inspection tasks, including the use of generative models such as Gaussian Splatting, diffusion models, or flow matching-based approaches. - Deep understanding and hands-on experience using physics engines and robotics simulation platforms (e.g., Isaac Lab, MuJoCo) to solve complex real-world robotic