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Reinforcement Learning Robotics Jobs in Fullerton, CA

FieldAI

Robotics Autonomy Engineer - Locomotion

Irvine, CA · On-site

$100K - $230K/yr

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 ...

FieldAI

Robotics Autonomy Engineer - Locomotion

Irvine, CA · On-site

$70K - $300K/yr

Architect and implement scalable reinforcement learning (RL) pipelines optimized for locomotion and ... Master's degree or higher in Robotics, Computer Science, Engineering, or related field (PhD ...

Socket.dev

Principal Robotics Engineer

Irvine, CA · On-site

$200 - $250/hr

... reinforcement learning. * Experience with simulation environments such as Isaac Sim, Gazebo, or ... Track record of deploying and maintaining robot fleets in harsh or unstructured real-world ...

FieldAI

Principal Robotics Engineer

Irvine, CA · On-site

$210K - $240K/yr

... reinforcement learning. * Experience with simulation environments such as Isaac Sim, Gazebo, or ... Track record of deploying and maintaining robot fleets in harsh or unstructured real-world ...

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Reinforcement Learning Robotics information

What is reinforcement learning in robotics?

Reinforcement learning in robotics refers to a type of machine learning where robots learn to perform tasks through trial and error, receiving feedback from their actions in the form of rewards or penalties. This approach allows robots to autonomously develop complex behaviors by interacting with their environment, rather than relying solely on pre-programmed instructions. Reinforcement learning is especially useful for tasks that are difficult to model explicitly, such as walking, grasping, or navigation. Over time, the robot improves its performance by maximizing the cumulative reward, leading to more efficient and adaptive behaviors.

What are some common challenges faced when implementing reinforcement learning algorithms in robotics projects?

One common challenge in this role is bridging the gap between simulation and real-world environments, as algorithms that perform well in simulation may not translate directly to physical robots due to unpredictable variables and hardware limitations. Additionally, ensuring the safety and stability of the robot during training is crucial, since trial-and-error learning can sometimes result in unintended behaviors or hardware damage. Collaboration with hardware engineers and domain experts is often necessary to fine-tune models, interpret results, and iterate on solutions. Overcoming these challenges requires patience, adaptability, and strong communication skills within a multidisciplinary team.

What are the key skills and qualifications needed to thrive as a reinforcement learning robotics engineer, and why are they important?

To thrive as a Reinforcement Learning Robotics Engineer, you need a strong background in robotics, machine learning, and programming, typically supported by a degree in computer science, engineering, or a related field. Expertise with frameworks like TensorFlow or PyTorch, experience with simulation environments (such as Gazebo or ROS), and familiarity with reinforcement learning algorithms are essential. Strong problem-solving skills, creativity, and effective communication set standout professionals apart in this rapidly evolving field. These skills enable engineers to develop intelligent robotic systems that adapt and learn efficiently, driving innovation and practical deployment in real-world environments.

What is the difference between Reinforcement Learning Robotics vs Machine Learning Engineer?

AspectReinforcement Learning RoboticsMachine Learning Engineer
Required CredentialsDegree in Robotics, Computer Science, or related fields; knowledge of reinforcement learningDegree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms
Work EnvironmentRobotics labs, manufacturing, autonomous systemsTech companies, data-driven projects, software development
Industry UsageAutonomous robots, industrial automation, researchData analysis, predictive modeling, AI applications

Reinforcement Learning Robotics focuses on applying reinforcement learning techniques to control and optimize robotic systems, often in physical environments. Machine Learning Engineers develop algorithms for a broad range of applications, including data analysis and predictive modeling. While both roles require knowledge of machine learning, Reinforcement Learning Robotics emphasizes robotics and real-world interaction, whereas Machine Learning Engineers work across various industries with software-based solutions.

What are popular job titles related to Reinforcement Learning Robotics jobs in Fullerton, CA?

For Reinforcement Learning Robotics jobs in Fullerton, CA, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Robotics jobs in Fullerton, CA look for?

The top searched job categories for Reinforcement Learning Robotics jobs in Fullerton, CA are:

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Cities near Fullerton, CA with the most Reinforcement Learning Robotics job openings:

Reinforcement Learning Robotics jobs near you

Robotics Autonomy Engineer - Locomotion

FieldAI

Irvine, CA • On-site

$100K - $230K/yr

Full-time

Re-posted 12 days ago

Job description

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California's robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.

About the Job
 
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 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.
What You'll Get To Do
  • 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
What You Have
  • 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
The Extras That Set You Apart
  • 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
$100,000 - $230,000 a year
Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
 
Why Join FieldAI in Irvine?
In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.
You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments.
 
Be Part of the Next Robotics Revolution
We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world.
 
Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.
 
We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected status.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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