Research Engineer, Robot Learning
At a glance
Mid-level AI Research role at 1X. San Carlos · full-time · $130,146–$300,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build sim-to-real reinforcement learning pipelines for humanoid robots, then train and test their policies
- Tune those pipelines to improve robot performance and shorten training time
- Model robot dynamics, sensors and contact behavior to bring simulated results closer to hardware
- Prepare human motion data for learning by collecting, cleaning and retargeting it
- Debug learned policies in simulation and on physical robots
What you bring
- A relevant master's degree or equivalent foreign credential, plus at least one year in robotics and machine learning or a related role
- Knowledge of rigid-body dynamics, reinforcement learning and sim-to-real methods for humanoid robots
- Ability to build production-quality Python training pipelines and debug real-time C++ software on robots
- Experience modeling contact dynamics in simulation and troubleshooting robot-system issues
- Understanding of forward and inverse kinematics for training humanoid systems with human motion data
Who this fits
This mid-level, full-time role is based on-site in San Carlos. It suits someone who combines robot learning with hands-on work across simulation and physical hardware.
From the employer
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:
Rigid body dynamics and/or dynamical systems for training, deploying and testing reinforcement learned policies for humanoid robots.
Implement production-grade Python code for reinforcement learning (RL) and other machine learning training pipelines.
Reinforcement learned algorithms, techniques, and sim2real approaches as they pertain to robotics to perform various human-like capabilities, including whole-body loco-manipulation.
Implement and debug real-time software in C++ deployed on robots to satisfy various real-time constraints.
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.
Root-cause analysis as it pertains to robotic systems and the ability to quickly troubleshoot issues.
Forward and inverse kinematics as they pertain to humanoid system training using human motion data.
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