Staff Robot Learning Engineer

Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge.

ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers.

With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible.

Learn more at www.analog.com and on LinkedIn and X.

• Develop and implement state-of-the-art AI algorithms for robotic manipulation, motion planning, perception and control.
• Integrate and calibrate perception systems (e.g., cameras, LiDAR, tactile sensors) with robotic platforms to enable enhanced task-awareness.
• Create simulation environments, deployable implementations, and evaluation frameworks to assess algorithm performance, scalability, and robustness.
• Establish data requirements, design data collection workflows, and support data collection, and curation for AI model development.
• Collaborate with AI researchers to translate cutting-edge algorithms into robust software implementations.
• Stay updated with the latest advancements in robotics software and AI to continuously enhance the team’s capabilities.
• Education: Bachelor's or Master's degree in Robotics, Computer Science, Electrical Engineering, or a related technical field, Ph.D. is preferred.
• Experience: 3+ years of professional experience in robot learning.
• Experience with robotic middleware platforms, especially ROS/ROS2.
• Proven hands-on experience developing simulation environments and synthetic data generation pipelines using NVIDIA Isaac Sim or MuJoCo
• Strong background in machine learning and robotics, and experience in:
• Reinforcement Learning and Imitation Learning for manipulation.
• Planning and control algorithms (e.g., A, MPC).
• Optimization techniques (e.g., linear/nonlinear optimization).
• Strong AI/ML Fundamentals: Familiarity with machine learning concepts, especially in the context of robot perception and control (e.g., PyTorch, TensorFlow).