Robotics Engineer - Intern

About CynLr SA

As a foundational technology building company in Robotics & AI, CynLr builds visual robots that can intuitively learn to pick & handle even unknown objects without requiring any prior training, just like a human baby fiddling with objects. CynLr calls this stack Object Intelligence (OI).

From fitting a screw to removing object out of its the plastic wrapper to automating the assembly of a car/gadget - every such object handling task that involves "adapting on the fly" is not prior trainable & thereby remains non-automatable across the industries. With OI's ability to learn on the fly, CynLr’s focus is to universally automate factories and eliminate the need for complicated custom machines to manufacture products. Thereby simplifying manufacturing into Universal Factories, which can be programmatically repurposed to produce a wide variety of Products

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CynLr envisions the future factories 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 manufacturability.

As a Robotics Engineering Intern at CynLr SA, you will contribute to the design, development, and optimization of our advanced robotic systems, specifically supporting the integration and control of CyRo, our multi-arm, dexterous, 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 planning 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 robotic 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 understanding of robot kinematics/dynamics, motion planning, or control systems.
• Interest in force control, vision-based robotics, and applied machine learning
• Strong Programming skills 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