About CynLr

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. Wit h 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.

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

As a Robotics Engineer , you will develop physics-based simulations, optimize multi-arm robotic workflows, and integrate AI-driven control systems. This role involves designing, validating, and optimizing robotic motion, perception, and manipulation algorithms for real-world applications. You'll collaborate across hardware, software, and ML teams to enhance robotic autonomy and efficiency

Physics-Based Simulation Development

• Develop comprehensive physics-based models of robotic systems, environments, and interactions.
• Create and validate dynamic models incorporating rigid body dynamics, contact physics, and material properties, and compliance for multi-arm robotic systems.
• Build digital twins of physical robots and environments to replicate real-world scenarios

Algorithm Development & Implementation

• Design, implement, and validate control and motion planning algorithms for multi-arm robots, focusing on customer manipulation and grasping tasks.
• Optimize and integrate kinematics, dynamics, and force-based control strategies for real-time applications.
• Support implementation of learning-based algorithms for real-time perception and manipulation tasks, including simulation-based testing and validation.

Machine Learning

• Leverage ML for robotic applications (e.g., perception, decision-making).
• Implement learning-based algorithms for real-time perception and manipulation tasks.

Testing, Validation & Optimization:

• Establish simulation validation protocols to bridge virtual and real-world performance, ensuring accuracy and reliability.
• Develop automated test sequences and metrics to validate algorithms across diverse scenarios with varying parameters (e.g., lighting, sensor noise, object positions, contact properties)
• Analyse simulation results to optimize robotic systems for performance, safety, and reliability, proposing design improvements (architecture, algorithms, or technologies).

Collaboration & Cross-Functional Support

• Collaborate with control engineers to validate and tune control systems in simulation.
• Collaborate with Algo and software/hardware teams to refine algorithms, identify and address sequencing errors, corner cases, and bottlenecks.
• Provide actionable insights from simulation analyses to guide system improvements.

Documentation & Reporting

• Document simulation methodologies, assumptions, and validation results.
• Provide detailed reports on system performance, optimization opportunities, and experimental findings

Must have an Understanding of

• Advanced physics-based modelling and numerical methods.
• Robot kinematics, dynamics, and control systems theory.
• Simulation validation and verification techniques.
• Sensor modelling (cameras, force/torque, etc.).
• Experience with motion planning algorithms.Engineering & Analysis.
• System dynamics modelling and error analysis.
• Test plan development and root cause analysis.
• Solution feasibility studies and model validation methodologies.

Good to Have

• Experiences: Machine learning frameworks (e.g., PyTorch, TensorFlow), Computer Vision, and real-time control system implementation.
• NVIDIA Isaac Sim/Omniverse, CoppeliaSim, Mujoco, PyBullet, PhysX, Gazebo, or similar physics-based simulation frameworks
• Python and C++ for motion scripting and automation.CAD software integration and version control systems (Git).