Senior Embedded Robotics Control Engineer - RealTime Motion

Genesis

Greater London

Presencial

GBP 80.000 - 120.000

Jornada completa

14 días+

Una candidatura completa en un minuto

Currículum y carta de presentación personalizados, listos para enviar.

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Descripción de la vacante

Genesis in London seeks a senior controls engineer to design, implement, and optimize the embedded control stack for general-purpose robots. You will design motion planning, trajectory optimization, and real-time state estimation pipelines.

You will lead debugging and validation on physical robots, bridge hardware and software, and explore deep learning, reinforcement learning, and vision-language-action models to advance robotics capabilities.

Formación

  • 8+ years of experience designing, implementing, and deploying advanced control algorithms on real robotic systems.
  • Strong knowledge of dynamics, kinematics, and optimal control.
  • Experience with sensors (IMUs, force/torque), actuators, and hardware interfaces.
  • Production-level C++ for safety-critical software.

Responsabilidades

  • Design, implement, and optimize the embedded control stack for general-purpose robots.
  • Design motion planning and trajectory optimization for dynamic locomotion and manipulation.
  • Build real-time state estimation pipelines for pose, contact, and force sensing.
  • Formulate and solve optimal control problems for high-performance behavior.
  • Build modular software frameworks enabling rapid simulation-to-hardware iteration.
  • Lead debugging, tuning, and validation of controllers on physical robots.

Conocimientos

Control systems engineering
C++ programming
State estimation
Dynamics & kinematics
Robotics hardware interfacing

Descripción del empleo

Genesis in London seeks a senior controls engineer to design, implement, and optimize the embedded control stack for general-purpose robots. You will design motion planning, trajectory optimization, and real-time state estimation pipelines.

You will lead debugging and validation on physical robots, bridge hardware and software, and explore deep learning, reinforcement learning, and vision-language-action models to advance robotics capabilities.

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