About CynLr

Just like a baby’s brain, CynLr Visual Intelligence stack makes Robots to instinctively see & pick any object under any ambience, without any training. (a demo video link).

Today, a robot that can fit a screw into a nut without slipping a thread, doesn’t exist. Imagine what it would take for a robot to assemble a Smartphone or a car by putting together 1000s of parts with varied shapes and weights, all in random orientations. Thus, factories become complex, needing heavy customization of their environment.

CynLr-enabled visual robots intuitively learn to handle even unknown objects, on-the-fly, eliminating the need for rigid fixtures, pre-training, or environment customization. This enables an universal alternative to custom automation thus simplifying factory lines into modular LEGO-like micro-factories that can be rapidly reconfigured as products change.

At the core of CynLr lies a fundamentally new approach to machine vision. Unlike conventional vision systems that rely on image recognition and heuristics, CynLr’ s Vision and ML stacks are deeply inspired by neuroscience, modelling how biological vision understands shape, geometry, and interaction rather than appearance alone. To support this, CynLr builds its hardware, sensors, compute pipelines, and learning stacks from scratch, tightly coupling perception, decision-making, and action.

This integration allows CynLr to operate in conditions that defeat traditional automation: variable lighting, cluttered environments, unknown objects, and high precision manipulation, unlocking automation use cases that have remained unsolved for decades. By rethinking vision as an intelligent, adaptive sense rather than a static tool, CynLr is redefining how robots perceive, reason, and interact with the physical world.

About the Role

This role places simulation at the centre of mechanical decision-making . You will work on mechanical simulations and physical modelling that directly shape product architecture, performance limits, and risk reduction before hardware is built. The work begins with deep understanding of real-world problems , translating them into physical and mathematical models that guide design choices, experiments, and validation.

We believe strong engineering lives in the balance between analysis, simulation, and physical reality . This role focuses on using simulations grounded in first principles to identify unknowns, explore trade-offs, and design targeted experiments that close the loop with real hardware.

You will own models across the full engineering cycle, from abstraction and simulation-driven design to validation and iteration, ensuring alignment with manufacturing constraints, testability, and real operating conditions . The goal is not impressive plots, but robust, manufacturable, and scalable products whose behaviour is understood in both models and hardware.

This role suits engineers who think in simulations , enjoy bridging models and real systems, and want their analytical work to directly influence real products

What You Will Do

What We Look For

This role spans broad mechanical ownership with a simulation-first lens , without expecting universal expertise. We look for engineers with deep strength in mechanical simulation and analysis , supported by enough system-level understanding to influence real designs

Core Strengths

• Strong grounding in mechanical fundamentals and physical intuition
• Deep expertise in: Structural, strength, fatigue, and vibration analysis, Thermal and Multiphysics behaviour of mechanical systems, Tolerance, stack-up, and robustness analysis, Statics, dynamics, kinematics, and multi-body system behaviour

Mindset

• Ability to treat simulations as thinking tools , not post-hoc validation
• Comfort translating uncertainty into models, assumptions, and testable hypotheses.
• Systems thinking that connects simulation results to design, manufacturing, and use cases
• Strong inclination toward validation, documentation, and closing the loop with hardware
• Ability to interpret control, algorithm, or motion-planning concepts as mechanical constraints

Tools

• Proficiency with CAD and 3D modelling tools (Fusion 360 preferred)
• Working familiarity with simulation and Multiphysics tools
• Exposure to motion systems, actuators, sensors, and basic electronics
• Experience with scripting or programming for analysis and automation is a plus

Specific tools are not the primary filter. First-principles thinking and the ability to build useful models matter most

Team Structure & Growth

The mechanical team includes simulation-focused engineers, design engineers, solutions engineers, and general mechanical engineers , working together to build complete physical systems. You will work in a highly cross-functional environment , collaborating closely with electronics, algorithms, software, manufacturing, and applications teams. Even in a simulation-focused role, your work remains tightly connected to design decisions, hardware behaviour, and deployment realities . As CynLr grows, there are opportunities to specialise de e ply , lead simulation-driven subsystems, or expand into system architecture, product design, or application engineering.

Why This Role Matters

Object manipulation in robotics remains unsolved because traditional assumptions and tools fall short . Here, simulations are not an academic exercise; they are how we understand reality before building it. Your work will directly shape how robots interact with the physical world , and in the process, redefine what simulation-driven mechanical engineering looks like in robotics.

Detailed JD Here: Mechanical Engineer - Simulations