Role overview
Job description
- 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
- 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
- 3+ years of experience in an industry or startup robotics setting
- Experience optimizing and deploying learning based controllers on resource constrained robotic platforms (ONNX Runtime, NVIDIA TensorRT, real time onboard inference)
- Publications or open-source contributions in locomotion, reinforcement learning, or control (e.g., CoRL, RSS, ICRA, IROS)
- Familiarity with ROS/ROS2 or custom middleware for real-time control
- Background in manipulation, loco-manipulation, or whole-body coordination
- Experience combining learned policies with model predictive control (MPC) or whole-body controllers
- Experience with motion imitation pipelines from motion capture or teleoperation data
- 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
About this listing
- Source: FieldAI's official careers site (via Lever) · last confirmed on the source 27 Oct 2026
- Posted 2 Jul 2025
Jobiba does not charge job seekers. Never pay to apply or share OTPs, card or bank details.
What they are looking for
Skills & requirements
Department · Engineering
Hiring company
FieldAI
FieldAI is the leader in deploying general-purpose robots in unstructured environments, providing a universal brain that works across many robots, and many tasks.
Related opportunities
Similar active roles
Relevant, currently active roles for this market.
$100K – $200K/yr
$100K – $200K/yr
$100K – $200K/yr
$100K – $200K/yr
$100K – $230K/yr
🔔 Get job alerts
New Robotics Autonomy Engineer jobs in Irvine, California, United States, straight to your inbox.
No spam · Unsubscribe anytime