Staff AI/ML Robotics Engineer - Environmental Perception

About the role

Analog Devices' Edge AI team in Ireland is looking for a Staff AI/ML Engineer with a strong background in robot perception, SLAM and sensor fusion. The role builds real-time systems that combine sensor technology with AI so machines can localize, map and understand complex environments, such as a mobile robot in a warehouse or a sensor inferring structure from sparse data. It is a hands-on position blending applied research with production-grade engineering.

Key responsibilities

• Design, prototype and deploy algorithms for SLAM, visual odometry and multi-sensor fusion (camera, IMU, LiDAR, encoders) in robotics and edge computing.
• Develop AI-driven methods for mapping, pose estimation, localization and semantic perception, with an emphasis on performance and generalization.
• Train and evaluate deep learning models for spatial understanding, integrating them with classical perception pipelines when needed.
• Work with real-world and simulated sensor data to test and refine models, and contribute to internal datasets and benchmarking tools.
• Collaborate with embedded, systems and software teams to bring perception solutions to production on resource-constrained edge platforms.
• Take part in project planning, code reviews and architectural discussions, and stay current with AI for robotics trends such as transformer-based perception, self-supervised learning and foundation models.

Required profile

• 6+ years of experience in AI, robotics or computer vision, including 4+ years focused on SLAM, sensor fusion or perception systems.
• Bachelor's degree in a relevant field (Robotics, Computer Science, Electrical Engineering); M.S. or Ph.D. preferred.
• Strong understanding of 3D geometry, motion estimation, sensor fusion and real-time system design.
• Hands-on experience building and deploying SLAM or VIO systems.
• Export licensing review may be required for some applicants; the role involves travel about 10% of the time and is a day shift.

Required skills

• Python and C++
• PyTorch, TensorFlow or ROS
• SLAM or VIO libraries such as ORB-SLAM, RTAB-Map, DSO, OpenVINS, Cartographer
• DevOps/MLOps tools: Docker, CI/CD pipelines, cloud platforms (Azure, AWS), Git
• Sensor data and simulators such as Gazebo, Isaac Sim or Unreal
• Bonus: integrating perception with control loops, path planning or ROS2, self-supervised or foundation model approaches, industrial robotics or AMRs, open-source contributions