Embodied AI Engineer (m/f)

At Abysalto, that’s not just a motto — it’s how we work.

We are expanding into Embodied AI — bringing intelligence into the physical world. With our recently acquired Unitree G1 humanoid robot and Unitree Go2 quadruped platform, we are starting R&D and pilot projects focused on autonomous behavior, perception, navigation, and AI-powered interaction.

We’re looking for an Embodied AI Engineer — someone who is excited about bridging robotics, AI, and real-world deployment.

Ready to dive in?

• Develop and integrate AI capabilities into humanoid and quadruped robotic platforms
• Work with ROS2 and DDS-based communication pipelines for robot control and perception
• Implement perception modules using LiDAR, depth cameras, and sensor fusion
• Develop autonomous behaviors using mjlab, Nvidia Isaac Lab/Sim, RL: navigation, obstacle avoidance, interaction logic
• Integrate AI components (LLMs, STT, TTS, vision models) into real-time robotic workflows
• Deploy and optimize AI workloads on embedded hardware (e.g. NVIDIA Jetson / edge devices)
• Build and test robotic applications in lab and pilot environments
• Collaborate with AI Backend, ML, and Data teams to connect robotics systems with cloud or on-prem AI services
• Debug real-world behavior issues: latency, control stability, sensor noise, environment constraints
• Contribute to internal R&D prototypes and client-facing demos

• Strong programming skills in Python (C++ is a bonus)
• Experience with ROS2 (topics, services, nodes, launch files), mjlab and/or Nvidia Isaac frameworks
• Understanding of robotic systems: kinematics, coordinate frames, control loops
• Experience working with sensors (LiDAR, IMU, depth cameras)
• Familiarity with Linux environments and networking
• Ability to debug complex distributed systems (robot + edge + backend)
• Strong problem-solving mindset and comfort with experimentation
• Good communication skills in English

• Experience with humanoid and/or quadruped platforms
• Experience with SLAM, navigation stacks, and motion planning
• Knowledge of reinforcement learning and imitation learning
• Experience with NVIDIA Jetson / edge AI optimization
• Familiarity with DDS-based middleware
• Experience integrating LLM-based interaction into robotic systems
• Exposure to simulation environments (e.g. Isaac Sim, mjlab), sim2sim, sim2real
• Experience working in research or R&D-heavy environments