Senior Applied AI Engineer

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 robotics problems.
• Experience with reinforcement learning, imitation learning, or policy optimization for robotic manipulation or process control.
• Hands-on experience with 3D data (point clouds, meshes, SDFs, CAD-derived geometry) and related tooling.
• Exposure to robotics inspection or quality assurance problems involving multimodal sensing (e.g., vision + force, vision + acoustics, vision + tactile, etc.).
• Experience with robotics middleware and tooling (e.g., ROS/ROS 2) and deployment on real robotic hardware.
• Prior experience working in industrial, manufacturing, or high-mix automation environments.
• A publication track record, or demonstrated interest in publishing applied research in venues such as ICRA, CoRL, RSS, IROS, RA-L, or T-RO, balanced with a strong bias toward real-world production impact.

Compensation and Benefits

• Base salary range, bonus or commission, and equity.
• Comprehensive benefits including medical, dental, vision, unlimited PTO, 401(k) plan + employer match, regular offsite events, a discretionary fund for enhancing productivity.