Research Engineer, Robot Learning
1X Technologies
San Carlos, CA · $130k–$300k a year · Posted October 23, 2026
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Pay compared with the market
This listing pays about $215,073 a year. Across 70 open research engineer listings that state pay, the middle half runs $220,000 to $422,000 and the median is $260,000. That is 17% below the median, below the middle half of those listings.
Listed pay is what employers advertise, not what people are paid.
About the job
Job Description: Develop sim2real reinforcement learning (RL) training pipelines for humanoid robots. Train and test robot policies via reinforcement learning for in-home humanoid robots. Optimize training pipelines for improved performance and training speed. Modeling of physical systems (including robot dynamics, sensor modeling, contact parameter tuning) to close the gap between simulation and physical systems. Collecting, cleaning and retargeting human motion data for use in RL training pipelines. Supporting the test and debug of RL policies in simulation and on physical hardware.
Requirements: Master’s degree or foreign degree equivalent in Robotics, AI/Machine Learning, Mechanical or Electrical Engineering, Computer Science, or related field and one (1) year of experience in a Robotics & Machine Learning occupation or related in the job offered.
Skills:
Experience and/or education must include:
1. Rigid body dynamics and/or dynamical systems for training, deploying and testing reinforcement learned policies for humanoid robots.
2. Implement production-grade Python code for reinforcement learning (RL) and other machine learning training pipelines.
3. Reinforcement learned algorithms, techniques, and sim2real approaches as they pertain to robotics to perform various human-like capabilities, including whole-body loco-manipulation.
4. Implement and debug real-time software in C++ deployed on robots to satisfy various real-time constraints.
5. Model physical systems, including multi-body hybrid dynamic systems with contact, in a simulated environment to identify gaps between simulation and hardware and the ability to understand how to accurately model the hardware in simulation.
6. Root-cause analysis as it pertains to robotic systems and the ability to quickly troubleshoot issues.
7. Forward and inverse kinematics as they pertain to humanoid system training using human motion data.