Principal Robotics AI/ML Engineer

Location: Munich

Team: Edge AI Group, Analog Devices Inc. (ADI)

Analog Devices’ Edge AI team is reimagining how machines perceive, decide, and act in the world around them. We’re developing intelligent systems that bring together best-in-class sensing with real-time AI.

We are looking for a Principal Engineer with deep domain expertise in robot policies, task planning, computer vision and decision-making under uncertainty to lead the development of real-world expert and generalist robot policies. Your work will drive how autonomous systems reason about goals, decompose tasks, and adapt in dynamic environments.

You will join a high-impact team developing foundational components for ADI’s next wave of robotics solutions. This is a hands-on leadership role for someone who thrives on solving open-ended challenges, defining technical roadmaps, and mentoring other engineers.

Responsibilities

• Lead the design and deployment of expert and generalist policies that enable industrial targeted tasksets to be enacted.
• Drive the design of task policies using techniques such as hierarchical planning, reinforcement learning, or hybrid methods.
• Develop real-world and simulation-based datasets for benchmarking and validation; guide sensor selection and system integration.
• Collaborate cross-functionally with embedded, hardware, and systems engineers to bring scalable solutions from research to production.
• Stay current with the state of the art in robotics, Edge AI, and self-supervised learning — and guide the team in adopting innovative technologies.
• Contribute to the broader AI platform architecture, CI/CD pipelines, and MLOps practices.
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Qualifications

• 10+ years of experience in robotics, computer vision, control theory or AI; with 3+ years in a technical leadership role.
• M.S. or Ph.D. in Robotics, Computer Science, Electrical Engineering, or related field.
• Demonstrated expertise in at least one of the following:
  • Task and motion planning
  • Policy learning methods
  • Robot foundation models
  • Decision making under uncertainty
  • Multi-sensor fusion (camera, LiDAR, IMU, encoders)
  • Robot control and integration
• Proven track record of deploying algorithms in real-world systems.
• Strong programming skills in Python, with experience in frameworks like ROS, PyTorch or Tensorflow.
• Deep knowledge in the areas of robot locomotion, robot kinematics and robot dynamics.
• Comfortable working with real-time systems, embedded platforms, or simulators such as Gazebo, Isaac Sim, MuJoCo, OpenAI Gym or Unreal Engine.
• Familiar with MLOps and software best practices: CI/CD, containerization (Docker), orchestration (Kubernetes), etc.
• Excellent communicator and collaborator — capable of leading multi-disciplinary teams and influencing stakeholders across hardware, software, and business domains.