Autonomy Robotics Research Internship: RL & Locomotion

Lever, Inc.; Irvine (CA); On-site; USD 62,000 - 83,000; Full time

Benefits offered by this job

Competitive pay

Onsite in Irvine, CA

Hands-on robotics research

Job summary

Field AI invites PhD students to join a Summer 2026 research internship focused on learning-based locomotion and planning for autonomous legged robots. You will work with research scientists to design experiments, implement RL pipelines, and validate ideas in simulation and on real hardware.

This fully onsite role is based in Irvine, CA, with opportunities to interface across robotics stack and contribute toward sim-to-real transfer, GPU-accelerated simulators, and robust deployment.

Qualifications

• Current PhD student in Robotics, CS, Mechanical Engineering, AI/ML, or a closely related field.
• Research experience in reinforcement learning for continuous control, locomotion, or learning-based planning.
• Strong foundation in contact dynamics, control theory, and kinematics.
• Proficiency in Python and/or C++, with robotics or ML tooling.
• Familiarity with physics-based simulators such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet.
• Experience designing experiments and evaluating results on robotic systems (simulation or hardware).
• Curiosity, initiative, and a strong interest in building autonomous systems that operate in the real world.

Responsibilities

• Design, implement, and evaluate reinforcement learning pipelines that tightly integrate locomotion control with learning-based planning.
• Explore how learned planners can inform and adapt locomotion behaviors across varied terrain and dynamic conditions.
• Contribute to research projects from early-stage ideas through simulation experiments and on-robot validation.
• Develop and refine sim-to-real transfer strategies, including domain randomization, system identification, and adaptive methods, for integrated locomotion-planning systems.
• Build and leverage GPU-accelerated simulation environments (Isaac Gym, Isaac Lab, MuJoCo) for scalable training and evaluation.
• Test and iterate on policies using real legged robot platforms in unstructured environments.
• Translate research concepts into working robotic systems tested on real hardware.
• Develop experimental setups and tooling to support data collection, evaluation, and reproducibility.
• Help ensure locomotion and planning systems are robust, field-relevant, and ready for iterative improvement.
• Work closely with systems engineers, perception experts, and embedded teams to close the loop between learning and execution.
• Incorporate real-world telemetry and field data to refine models and improve generalization.
• Engage with researchers and engineers across the team to align experiments with broader autonomy goals.
• Prototype quickly, run experiments in simulation and on hardware, and analyze results rigorously.
• Balance exploratory research with concrete deliverables over the course of the internship.
• Debug system-level issues spanning simulation, software, hardware, and learning.

Skills

Python

C++

Reinforcement Learning

Locomotion

Planning

ROS

PyBullet

MuJoCo

Isaac Gym

Isaac Lab

Education

PhD student in Robotics/CS/ME/AI

Tools

ROS2

PyBullet

MuJoCo

Isaac Gym

Isaac Lab

Job description

Field AI invites PhD students to join a Summer 2026 research internship focused on learning-based locomotion and planning for autonomous legged robots. You will work with research scientists to design experiments, implement RL pipelines, and validate ideas in simulation and on real hardware.

This fully onsite role is based in Irvine, CA, with opportunities to interface across robotics stack and contribute toward sim-to-real transfer, GPU-accelerated simulators, and robust deployment.