Robotics & Reinforcement Learning Engineer

Company Description PAL Robotics is a leading service robotics company based in sunny Barcelona. Our goal is to enhance people’s quality of life through robotics and automation technologies. We have over 20 years of experience in the robotics field and offer daily challenges to everyone in our team to help them grow. Job Description We are looking for a Robotics Engineer specialized in Reinforcement Learning (RL) to develop, deploy, and validate control policies on real robotic systems. You will work at the intersection of simulation and hardware, building robust policies in simulation (MuJoCo / MJLab) and transferring them to real robots with high reliability. This role requires strong expertise in policy learning, sim-to-real transfer, and system identification, as well as hands-on experience validating behaviors on physical platforms. Primary duties: - Develop RL-based control policies for robotic systems using MuJoCo and MJLab. - Design and train locomotion and manipulation policies, including walking, balancing, grasping, and object interaction. - Implement and compare pure reinforcement learning approaches with imitation learning methods like behavior cloning and offline RL. - Deploy and validate trained policies on real robot hardware to ensure effective performance. - Bridge the sim-to-real gap using system identification, domain randomization, and actuator/dynamics modeling. - Analyze failures and iterate on models, reward functions, and training setups to improve performance. - Integrate policies into existing robotics stacks, such as ROS 2-based systems. - Collaborate across teams, interfacing with control, perception, and hardware specialists. Qualifications Mandatory Requirements: - MSc or PhD in Robotics, Machine Learning, Control, or a related field. - Strong experience in Reinforcement Learning specifically applied to the field of robotics. - Hands-on experience with MuJoCo, with MJLab proficiency considered a strong plus. - Proven experience deploying learned policies on real robotic systems and hardware. - Solid understanding of robot dynamics, control, policy optimization (PPO, SAC, etc.), and imitation learning. - Experience with sim-to-real transfer techniques, such as domain randomization and system identification. - Proficient programming skills in both Python and C++. - Familiarity with robotics frameworks, particularly ROS 2. Other valued skills (not mandatory): - Experience with humanoid or legged robots - Knowledge of actuator modeling and transmission dynamics - Experience designing robust policies under uncertainty - Familiarity with GPU-based training pipelines - Background in optimization or model-based control (MPC is a plus) Additional Information Job conditions We offer a competitive compensation package, including salary, benefits, and opportunities for professional development. You will be part of a dynamic and international team in a constantly growing and developing