Назад
Company hidden
2 месяца назад

AI Robotics Engineer

Формат работы
onsite
Тип работы
fulltime
Грейд
lead
Английский
b2
Страна
Germany
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
AI Robotics Engineer (Multimodal Learning and Sim-to-Real): Designing and building multimodal learning systems that fuse vision, depth, tactile, IMU, language, and robot-state inputs into executable actions for dexterous robots with an accent on simulation infrastructure, data pipelines, and physical-hardware deployment. Focus on closing the sim-to-real gap, developing imitation-learning and deep-RL policies, and translating research problems into engineering milestones.

Location: Munich, Germany
Work format: On-site

Company

hirify.global is hiring for its AI department to develop learning systems that run on physical robots.

What you will do

  • Design and build multimodal learning systems that combine camera, depth, tactile, IMU, language, and robot-state inputs into executable actions.
  • Define policies for dexterous robotic hands and develop learning systems for physical robot hardware.
  • Build and maintain GPU-accelerated simulation environments and scalable data pipelines.
  • Lead engineering work to transfer policies from simulation to physical robots.
  • Translate research problems into engineering milestones and guide engineers across ML, robotics, and hardware teams.
  • Support the growth of junior engineers.

Requirements

  • 6+ years of experience in computer science or a related engineering field, including meaningful experience delivering AI systems on physical robotic hardware.
  • Hands-on experience designing or co-leading multimodal manipulation systems combining vision, language, tactile, and proprioceptive inputs.
  • Experience building simulation infrastructure with Isaac Lab and Isaac Sim or MuJoCo for reinforcement learning and sim-to-real transfer.
  • Deep practical knowledge of imitation learning, including diffusion policies, deep reinforcement learning, and hybrid learning approaches on real robot hardware.
  • Experience with heterogeneous, high-frequency sensor-data pipelines covering teleoperation, tactile, vision, depth, and robot-state data.
  • Strong Python and C++ skills, plus experience with ROS2 and embedded or real-time systems.

Nice to have

  • Experience with high-DOF, tendon-driven, or tactile-heavy dexterous hands.
  • Familiarity with VLA or vision-language-action architectures and large-scale pre-training workflows.
  • Contributions to the robotics or AI research community, including ICRA, IROS, CoRL, or NeurIPS.

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