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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Robotic Software Controls Engineer - **Company:** Schaeffler Group USA Inc. - **Location:** Troy, MI, United States - **Experience:** Starter - **Contract:** Internship / Graduate position - **Skills:** Adobe InDesign, Artificial Intelligence, Systems Engineering, C++ (Programming Language), Code Review, Communications Protocols, Computer Engineering, Software Debugging, Embedded Software, Ethernet, EtherCAT, Python (Programming Language), Kinematics, MATLAB, Microsoft Dynamics, Real-Time Operating Systems, Tensorflow, Software Engineering, Reinforcement Learning, Digital Twin, PIC Microcontroller, Real Time Systems, Pytorch, Git, Software Version Control - **Published:** September 24, 2026 - **Apply:** https://jobs.schaeffler.com/talentcommunity/apply/1432667333/?locale=en_US ## About the Role * Bachelor's degree in Software Engineering, Computer Engineering, Electrical Engineering, Robotics, Mechatronics, or a closely related field (graduating or recently graduated). * 0-2 years experince with control systems, embedded software, and real-time systems. (Professional or Academic Exp.) * Proficiency in C/C++ and Python. * Familiarity with robotics concepts (kinematics, dynamics, actuators, sensors) through coursework, senior design, or personal projects. * Experience with simulation tools or environments (e.g., Gazebo, MuJoCo, Isaac Sim, MATLAB/Simulink, or equivalent). * Solid understanding of software development practices (version control with Git, debugging, testing). * Ability to work collaboratively in a multidisciplinary team and communicate technical ideas clearly. * Must be legally authorized to work in the US. Preferred Qualifications * Previous co-op or internship experience in robotics, actuator controls, simulation, digital twins, reinforcement learning, or a closely related field. * Coursework or project experience involving motor control, FOC, impedance control, or state estimation (Kalman filters, observers). * Exposure to reinforcement learning frameworks (e.g., Stable Baselines, RLlib, or custom PyTorch/TensorFlow pipelines) and sim-to-real techniques. * Experience with ROS/ROS 2, real-time operating systems, or industrial communication protocols (EtherCAT, EtherNet, etc.). * Familiarity with digital twin concepts, system identification, or physics-based modeling of electromechanical systems. * Hands-on experience with microcontrollers, motor drivers, or robot hardware through labs, internships, or personal projects. * Strong mathematical background (linear algebra, differential equations, optimization). ## Description We are seeking a motivated Computer Engineering graduate to join our humanoid robotics team. In this role you will contribute to the design, implementation, and validation of advanced actuator control software and features for humanoid robots, while also supporting the creation and maintenance of high-fidelity digital twins used in reinforcement learning pipelines. This is an excellent opportunity for a recent graduate to work at the intersection of embedded controls, robotics, and simulation-driven AI., * Develop, implement, and refine real-time control algorithms and features for humanoid robot actuators (position, torque, impedance, and hybrid modes). * Write clean, efficient, and well-documented embedded and high-level software for actuator control loops, state estimation, and safety monitoring. * Collaborate with mechanical, electrical, and systems engineers to integrate new actuator capabilities into the overall humanoid platform. * Support the development and validation of digital twins of actuators, including accurate modeling of dynamics, friction, compliance, thermal effects, and sensor characteristics. * Assist in integrating actuator digital twins into reinforcement learning environments for policy training, sim-to-real transfer, and control co-design. * Perform system identification, parameter estimation, and correlation between physical actuators and their digital counterparts. * Conduct testing, debugging, and performance analysis on both hardware and simulation platforms. * Document designs, algorithms, test results, and lessons learned; participate in design reviews and code reviews. * Stay current with relevant advances in robot control, simulation, and reinforcement learning techniques applicable to humanoid systems. ## Related Videos - [Robots are coming into the wild! 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