Robotics Modelling & Simulation Engineer

Cubiq Recruitment

Bayern

Vor Ort

EUR 90.000 - 120.000

Vollzeit

vor 43 Stunden
Sei unter den ersten Bewerbenden
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Zusammenfassung

Cubiq Recruitment is assisting a Munich‑based robotics client to hire a Modelling & Simulation Engineer. You will own the digital twin, derive load/torque specs from first principles, and guide hardware teams to build correct‑by‑design systems.

The role requires 5+ years in robotics, masterful FEA/MBSE, and hands‑on experience with Gazebo, MuJoCo, and Matlab to drive sim‑based validation prior to prototyping.

Qualifikationen

  • 5+ years of experience in robotics context with classical physics and multi‑body dynamics.
  • Strong simulation expertise and ability to design virtual environments.
  • Experience with turning physics into hardware design guidance.

Aufgaben

  • Digital Twin Stewardship: maintain high‑fidelity URDF models and simulations.
  • System Modeling: identify torque and load specs for hardware design.
  • MBSE Implementation: use MBSE to predict physical limits and optimise performance.
  • Method Development: create tools and methods to accelerate engineering velocity.

Kenntnisse

FEA
MBSE
Matlab
Gazebo
MuJoCo
URDF

Tools

URDF

Jobbeschreibung

Robotics Modelling & Simulation Engineer | Munich, Germany |

We are looking for a Modelling & Simulation Engineer to work for one of our Robotics clients. They are well‑capitalized, operating in stealth, and are now assembling their core team.

Founded by entrepreneurs with backgrounds in scaling digital products and advanced robotics, the team is bolstered by senior leadership from top-tier AI research labs and industrial tech companies.

Driven by a mission to create versatile and trustworthy embodied AI for physical labor, fuse deep engineering mastery in AI and mechatronics with a rapid-execution culture. They're addressing the labor shortage by closing the gap to production‑grade deployment.

You are the architect of their "Digital Twin," bridging the gap between theoretical physics and physical reality. You will own the mathematical integrity of our robots, ensuring that every load spec and torque requirement is derived from first principles. Your work provides the roadmap for our mechanical and actuator teams to build hardware that is "correct by design".

Key responsibilities
  • Digital Twin Stewardship: Build and maintain high-fidelity URDF models and simulation environments that serve as the single source of truth for the entire engineering organization.
  • System Modeling: Conduct static and dynamic analyses to identify critical torque and load specifications, feeding these requirements directly into the hardware design cycle.
  • MBSE Implementation: Utilize Model-Based Systems Engineering to predict physical limitations and optimize system-level performance before a single part is machined.
  • Method Development: Create "best-in-class" simulation tools and methodologies that accelerate our engineering velocity.
Your background

We seek builders ready to move beyond demos to real-world deployment - professionals who value precision and reliability as much as innovation.

  • Experience: 5+ years of experience with a profound understanding of classical physics and multi‑body dynamics in the robotics context.
  • Core Skills:
  • Simulation Expert: Mastery of FEA, MBSE, Matlab, Gazebo, MuJoCo, (decode complex physical constraints).
  • Robotics Simulation & Control Engineering: Deep expertise in mechatronics and classical system identification (model structure/parameter selection), with a proven ability to design virtual environments, deploy classical and ML-based control stacks, and execute simulation-based experiments to optimize kinematics, packaging, and hardware configurations prior to physical manufacturing.
Mindset
  • Collaborative Communicators: Individuals who can bridge the gap between mechanical, electrical, and software teams to ensure seamless system integration.
  • Results-Oriented Designers: Professionals with a "hands‑on" mindset who enjoy troubleshooting prototypes and refining designs based on empirical test data.
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