Motor Control Engineer – Robotic Actuators

Confidential

Palo Alto, California, United States permanent

Posted: January 30, 2026

Required Skills

Job Description

About 1X
We’re an AI and robotics company based in Palo Alto, California, on a mission to build a truly abundant society through general-purpose robots capable of performing any kind of work autonomously.

We believe that to truly understand the world and grow in intelligence, humanoid robots must live and learn alongside us. That’s why we’re focused on developing friendly home robots designed to integrate seamlessly into everyday life.

We’re looking for curious, driven, and passionate people who want to help shape the future of robotics and AI. If this mission excites you, we’d be thrilled to hear from you and explore how you might contribute to our journey.

Role Overview

We are looking for a Motor Control Engineer with deep expertise in motor control and control theory to drive the performance of our robotic actuators. In this role, you will be responsible for defining, developing, and tuning motor control strategies that directly impact torque quality, efficiency, thermal behavior, and overall actuator performance.

You will work closely with electrical, mechanical, and firmware engineers within the team to translate control concepts into robust, real-world actuator behavior as well as with the higher level controls and AI engineers to optimize system level robot performance.

Responsibilities

• Develop and own motor control strategies for robotic actuators

• Design, analyze, and tune control loops (current, torque, speed, and higher-level loops)

• Evaluate control performance across operating conditions, including nonlinearities and saturation effects

• Work closely with firmware engineers to translate control algorithms into embedded implementations

• Collaborate with electrical engineers on sensing, actuation, and control-relevant hardware decisions

• Support actuator characterization and tuning on dynos and test benches

• Analyze test data to improve control performance, robustness, and stability

• Document control architectures, assumptions, and tuning methodologies

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