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CynLr envisions the future factori es to be decentralised, micro factories (not the Giga Factories ) that could rather be hosted in your street-ends; opening up the possibility of Personalised Products – liberating design of products from the constraints of manufacturabilit y.

A s a Robotics Engineering Int ern at CynLr SA, you will contribute to the design, development, and optimization of our advanced robotic syst ems, specifically supporting the integration and control of CyRo, our multi-arm, dextero us, vision-guided robotic platform . This internship offers an opportunity to work on real-world projects, learning from experienced engineers, and start a career towards making a tangible impact in the field of robotic automation, in real-time.

About Role

You will join a multi-disciplinary team and contribute to the design, development, testing, and deployment of robotic systems and applications in one or more of the following focus areas:

• Assisting in the development and integration of motion plan ning and control algorithms for multi-arm robotic manipulation.
• Supporting the development of physics-based simulation models for robotic systems, environments, and interactions.
• Supporting real-world experiments involving object grasping , in-hand manipulation, and assembly.
• Building and validating physics-based simulation models using tools such as Isaac Sim, MuJoCo, CoppeliaSim or PyBullet .
• Participating in the design and testing of adaptive and force-sensitive grasping strategies .
• Collaborating in implementing vision-guided robo tic manipulation workflows .
• Analysing experimental results & iterative improvements of algorithms & hardware setup
• Documenting methodologies, assumptions, and experimental findings.

Requirements in Practice

Must have:

Currently enrolled in or recently graduated in Robotics, Mechanical/Electrical Engineering, Computer Science, or related fields, who meet some of the following criteria:

• Good understandi ng of robot kinematics/dynamics, motion planning, or control systems.
• Interest in force control, vision-based robotics, and applied machine learning
• Strong Programming skil ls in Python and/or C++.
• Familiarity with robotic simulation tools ( e.g., Gazebo, MuJoCo, Isaac Sim), or real-time control frameworks.
• Comfortable with hardware troubleshooting and hands-on debugging.
• Strong analytical thinking and a “build-and-iterate” mindset.
• Self-motivated and eager to learn in a fast-paced, hands-on environment

Good to have:

• Experience with force-torque sensors or admittance/impedance control .
• Exposure to machine learning ( Reinforcement Learning , Deep Learning ) or computer vision frameworks (e.g., PyTorch, TensorFlow, OpenCV ).
• Familiarity with CAD tools and basic mechanical design.
• Prior experience working with multi-arm robots or custom robotic setups.
• Participation in robotics competitions or personal robotics projects