AIM provides an embodied AI platform that retrofits bulldozers, excavators and other heavy equipment with plug-and-play hardware and software to enable autonomous, zero-entry fleets and AI-optimized site operations.
ABOUT AIM Everything humanity depends on is mined, dug, or grown. At AIM, we are building the autonomous linchpin of civilization. We transform heavy machinery—bulldozers, loaders, excavators—into AI-powered fleets that operate continuously, safely, and at peak performance in the world’s harshest environments. AIM runs production mines, large scale infrastructure builds, and defense operations as a TRL9 hardened system, not a science experiment. Built by engineers from mining, construction, Waymo, SpaceX, Google and Tesla, AIM enables scalable earthmoving, turbocharging the global economy’s physical foundation. AIM is backed by some of the most sophisticated capital in the world, including General Catalyst, Khosla Ventures, Elad Gil, Human Capital, Ironspring Ventures, Mantis, DCVC. Learn more about AIM here. https://aim.vision/about RESPONSIBILITIES - Automate, Develop, implement, and validate advanced control and learning algorithms for real-world embodied robotic systems. - Design and conduct experiments to expand control robustness, precision, and adaptability across diverse tasks and environments. - Combine classical and learning-based control methods (e.g., MPC, IL, RL) for scalable and reliable skill acquisition. - Collaborate with perception and systems engineers to integrate AI control stacks into production platforms. - Leverage simulation, digital twins, and expert demonstrations to accelerate control policy development and deployment. - Stay up-to-date on cutting-edge research in control theory, reinforcement learning, and embodied AI. QUALIFICATIONS - 5+ years industry experience - Proven experience delivering production-level robotic control systems in real-world deployments (e.g., autonomous vehicles, manipulators, humanoid or mobile robots). - Strong foundation in modern control techniques (e.g., MPC, adaptive control, system identification) and their integration with learning-based methods. - Deep understanding of reinforcement learning, imitation learning, and optimization for dynamic systems. - Proficiency in Python and familiarity with C++ for real-time robotics applications. - Experience working with high-fidelity simulators (e.g., Isaac Sim, Omniverse, Mujoco) for control development and testing. - Excellent communication and teamwork skills, with the ability to bridge between AI research and robotic systems engineering.