The posting
About the Internship
Field AI is building the future of autonomy—from rugged terrain to real-world deployment. We’re on a mission to develop intelligent, adaptable robotic systems that operate beyond simulation and thrive in unpredictable environments.
We are offering a Summer 2026 internship focused on learning-based locomotion and planning for PhD students interested in advancing autonomous legged robot capabilities. As a research intern, you will work at the intersection of reinforcement learning, locomotion control, and learned planning, developing integrated systems that enable robots to move and navigate intelligently through complex, unstructured environments.
You will collaborate closely with Field AI research scientists and engineers to design experiments, develop locomotion and planning systems, and validate ideas in simulation and on real hardware. This internship emphasizes building tightly integrated learning-based systems that connect low-level locomotion with high-level planning, translating research into practical, deployable capabilities for real-world robotics.
What You’ll Get To Do
- 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.
What You Have
- Current PhD student in Robotics, Computer Science, 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 experience using 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.
The Extras That Set You Apart
- Hands-on experience with legged robot platforms (quadrupeds, wheeled-quadrupeds, bipedal systems, or exoskeletons).
- Experience with sim-to-real transfer for locomotion or planning policies.
- Background in learning-based planning, motion planning, or terrain-adaptive control.
- Familiarity with ROS or ROS2.
- Publications, preprints, or open-source contributions in locomotion, RL, planning, or control.
- Experience deploying neural network controllers on resource-constrained or real-time robotic platforms.
- Interest in bridging cutting-edge research with practical, field-ready robotic systems.
Field AI Onsite Work Philosophy
At Field AI, we believe the most effective way to collaborate and solve complex challenges is by working together in person. This is a fully onsite role, and candidates will be expected to work from our Irvine, CA office. In-person engagement is essential to our success, and we offer flexible working hours to support focus and work-life balance.
Why Join Field AI?
We are solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.
You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals.
Be Part of the Next Robotics Revolution
To tackle such ambitious challenges, we need a team as unique as our vision — innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We’re seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.
Join us, shape the future, and be part of a fun, close-knit team on an exciting journey!
We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status.
