AIM
Senior Embodied AI Engineer - Controls
Seattle · Senior
Sponsorship not specifiedDetected 136 days ago
PythonC++AlgorithmsRoboticsControlsResearchCommunicationAdaptability
About the role
- ABOUT AIM Everything humanity depends on is mined, dug, or grown.
- 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.
- Built by engineers from mining, construction, Waymo, SpaceX, Google and Tesla, AIM enables scalable earthmoving, turbocharging the global economy's physical foundation.
Responsibilities
- Design and conduct experiments to expand control robustness, precision, and adaptability across diverse tasks and environments.
- Collaborate with perception and systems engineers to integrate AI control stacks into production platforms.
- At AIM, we are building the autonomous linchpin of civilization.
- AIM runs production mines, large scale infrastructure builds, and defense operations as a TRL9 hardened system, not a science experiment.
Requirements
- 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).
- 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.
- 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.
Benefits
- Learn more about AIM here. https://aim.vision/about
- Automate, Develop, implement, and validate advanced control and learning algorithms for real-world embodied robotic systems.
- Combine classical and learning-based control methods (e.g., MPC, IL, RL) for scalable and reliable skill acquisition.
- Stay up-to-date on cutting-edge research in control theory, reinforcement learning, and embodied AI.
This listing is sourced directly from AIM's careers page and normalized into a canonical job model.