Robotics Autonomy Engineer - Locomotion

Field AI is transforming how robots interact with the real world. We are building risk‑aware, reliable, and field‑ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data‑driven approaches or pure transformer‑based architectures, and are charting a new course, with already‑globally‑deployed solutions delivering real‑world results and rapidly improving models through real‑field applications.

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. As our Robotics Autonomy Engineer – Locomotion
, you’ll lead the development and deployment of state‑of‑the‑art reinforcement learning‑based controllers for legged and humanoid robots. You'll be part of a deeply technical team advancing real‑world robotic capabilities through cutting‑edge research, simulation tools, and field validation.

If designing locomotion systems that can navigate complex, dynamic environments excites you, and you want to work where your code hits the ground (literally)—this is your role. This is Field AI.

• 1. Design RL-Based Locomotion Control Pipelines
• Architect and implement scalable reinforcement learning (RL) pipelines optimized for locomotion and manipulation.
• Integrate physics‑based simulation environments (Isaac Gym, Isaac Lab, Mu Jo Co ) with custom training workflows.
• Optimize reward functions, policy architectures, and sim‑to‑real transfer methods.

• 2. Develop and Test Locomotion Behaviors
• Create agile and robust policies for legged or humanoid robots in simulated and real‑world conditions.
• Solve challenges in balance, contact‑rich dynamics, and high‑DOF coordination.
• Drive iterative testing across terrain variability and unstructured environments.

• 3. Own Simulation and Evaluation Environments
• Build scalable training environments using Isaac Sim and Isaac Gym.
• Automate evaluation across domain‑randomized scenarios and domain adaptation protocols.
• Maintain high‑performance simulation infrastructure for rapid prototyping and validation.

• 4. Collaborate Across Perception, Planning, and Hardware Teams
• Work closely with systems engineers, perception experts, and embedded teams to close the loop between learning and execution.
• Incorporate real‑world telemetry to refine models and improve generalization.
• Lead deployment workflows from experiment to field robot testing.

• Master’s degree or higher in Robotics, Computer Science, Engineering, or related field (PhD a strong plus).
• Deep expertise in reinforcement learning, especially for continuous control tasks.
• 2+ years of experience developing and deploying locomotion policies for robotic systems.
• Proficiency with simulation tools such as Isaac Gym, Isaac Lab, Mu Jo Co , or PyBullet.
• Strong understanding of contact dynamics, control theory, and kinematics.
• Experience with legged and/or humanoid robots (quadrupeds, bipedal systems, or exoskeletons).
• Solid Python and/or C++ development skills in Linux‑based environments.
• Familiarity with machine learning frameworks (PyTorch, Tensor Flow).

• 3+ years of experience in an industry or startup robotics setting.
• Experience with real‑world deployment of learned locomotion controllers.
• Publications or open‑source contributions in locomotion, RL, or control.
• Familiarity with ROS or custom middleware for real‑time control.
• Background in manipulation or whole‑body coordination.
• Experience of deploying neural network models on robotic platforms.
• Experience debugging sim‑to‑real issues at scale.
• Contributions to reinforcement learning libraries or simulation platforms.
• Prior work on multi‑agent learning or terrain‑adaptive control systems.

Our salary range is generous and we take into consideration an individual’s background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job‑related knowledge, skills, and experience.

We are solving one of the world’s most complex…