About the job
About 1X
At 1X, we are at the forefront of AI and robotics innovation, located in the heart of Palo Alto, California. Our mission is to revolutionize society by creating versatile robots capable of performing a wide range of tasks autonomously. We envision a future where humanoid robots learn and grow alongside humans, enhancing everyday life with their capabilities.
Our commitment to developing user-friendly home robots that integrate into daily routines drives us to seek passionate, inquisitive, and motivated individuals who are eager to contribute to the future of robotics and AI. If our vision resonates with you, we would love to connect and explore how you can become a part of our journey.
Role Overview
We are in search of a skilled Motor Control Engineer with profound knowledge of motor control and control theory. Your role will be pivotal in enhancing the performance of our robotic actuators. You will be tasked with defining, developing, and fine-tuning motor control strategies that significantly influence torque quality, efficiency, thermal behavior, and overall actuator performance.
Collaboration is key in this role as you will work closely with electrical, mechanical, and firmware engineering teams to translate control concepts into effective, real-world actuator performance. Additionally, you will partner with AI and higher-level control engineers to optimize the overall performance of our robotic systems.
Responsibilities
Lead the development and ownership of motor control strategies for robotic actuators.
Design, analyze, and refine control loops (current, torque, speed, and higher-level loops).
Assess control performance across various operating conditions, including nonlinearities and saturation effects.
Collaborate with firmware engineers to implement control algorithms in embedded systems.
Work with electrical engineers regarding sensing, actuation, and hardware considerations relevant to control.
Support actuator characterization and tuning on dynamometers and test benches.
Analyze test data to enhance control performance, robustness, and stability.
Document control architectures, foundational assumptions, and tuning methodologies.

