Research Engineer, Robot Learning

1X; San Carlos (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

1X in California seeks a robotics ML engineer to develop sim2real RL training pipelines for humanoid robots, train and test policies in simulation and on hardware, and optimize training speed. You will model physical dynamics, collect and retarget motion data, debug RL policies, and work across Python/C++ real-time systems to close the sim-to-real gap.

Join a team focused on advancing humanoid robotics through rigorous simulation, hardware testing, and data-driven optimization in a fast-paced

Qualifications

• Master's degree in a relevant field and 1 year of Robotics/ML experience.
• Experience implementing production-grade Python code for RL/ML pipelines.
• Knowledge of sim2real, RL algorithms, and humanoid capabilities such as loco-manipulation.
• Experience implementing and debugging real-time C++ software on robots.
• Modeling physical systems including multi-body dynamics with contact in simulation.
• Ability to perform root-cause analysis for robotic systems.
• Familiarity with forward/inverse kinematics for humanoid motion data.

Skills

Rigid body dynamics

Python RL pipelines

Sim2real RL

C++ real‑time robotics

Physical system modeling

Root-cause analysis

Kinematics (F/K)

Education

Master's degree in Robotics/AI/ML/Engineering/CS

Tools

Python

C++

Job description

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.