Robotics Autonomy Engineer, Locomotion
FieldAI· Engineering – Autonomy
Hybrid (Irvine, CA)$100k - $230kFull-timePosted 3mo ago
About the role
Field AI is building autonomous robotic systems for real-world deployment. The Robotics Autonomy Engineer – Locomotion leads the development and deployment of state-of-the-art controllers for legged and humanoid robots, combining research with field validation and hardware testing.
Responsibilities
- Architect and implement scalable reinforcement learning (RL) pipelines for locomotion and whole-body control on legged and humanoid robots
- Design policy architectures, training curricula, and domain randomization strategies that close the sim-to-real gap
- Integrate GPU-accelerated, physics-based simulation environments with custom, distributed training workflows
- Train locomotion policies from human motion data using imitation learning and motion retargeting, and distill them into compact policies that run on the robot
- Create agile, robust, and terrain-aware (perceptive) locomotion behaviors for quadruped and humanoid platforms, and validate them on real hardware
- Solve real-world challenges in balance and push recovery, contact-rich dynamics, high degree of freedom (DOF) whole-body coordination, and terrain variability
- Automate evaluation across domain-randomized scenarios and maintain simulation infrastructure that enables rapid prototyping, validation, and reproducibility
- Work closely with systems engineers, perception experts, and embedded teams, incorporating real-world telemetry and field data to continuously improve generalization
- Lead deployment workflows from experiment through lab testing to field robot validation
Qualifications
- Master’s degree or higher in Robotics, Computer Science, Engineering, or related field (PhD strongly preferred)
- Deep expertise in reinforcement learning for continuous control
- 2+ years of experience developing and deploying locomotion policies on real robotic systems (preferred)
- Hands-on experience with legged robot platforms (quadrupeds, bipedal/humanoid systems, or exoskeletons)
- Proficiency with simulation tools such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet
- Strong command of sim-to-real transfer and a track record of bridging the gap successfully
- Solid understanding of contact dynamics, control theory, and kinematics
- Strong Python and/or C++ development skills in Linux-based development environments
- Familiarity with machine learning frameworks (PyTorch, JAX, TensorFlow)
- A passion for building things that move in the real world
Skills
Required:Reinforcement learning for continuous controlLocomotion policy developmentSimulation tool proficiencySim-to-real transferPython and/or C++Machine learning frameworks
Last updated Oct 22, 2026. Interested? Apply on the company site.
About FieldAI
Robot intelligence built for the real-world.
FieldAI is the leader in deploying general-purpose robots in unstructured environments, providing a universal brain that works across many robots, and many tasks.
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