Principal AI/ML Robotics Simulation Engineer
Limerick, County Limerick, Ireland · Full Time
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- Experience
- 10+ yrs
- Salary
- —
- Openings
- 1
- Posted
- il y a 5 heures
- Work mode
- In office
- Education
- M.S. or Ph.D. in related field
- Resume
- Required to apply
Where you'll work
Job description
About Analog Devices
Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader dedicated to bridging the physical and digital worlds through cutting-edge technology. The company combines analog, digital, AI, and software innovations to address challenges in climate change, connectivity, automation, robotics, mobility, healthcare, energy, and data centers. With revenues surpassing $11 billion in fiscal year 2025, ADI empowers innovators to push the boundaries of possibility.
Role Overview
The Edge AI division at ADI aims to revolutionize machine perception and interaction by developing real-time intelligent systems utilizing advanced sensor technologies and AI at the edge. We are seeking a Principal AI/ML Robotics Simulation Engineer to drive the creation of detailed robotic simulations and real-world validation methods for fixed-arm and humanoid robots. This position requires expertise in sensor and mechanical simulations combined with AI-driven data processes to guarantee dependable system performance both virtually and physically.
Primary Responsibilities
- Design and enhance sensor simulation models such as ToF, RGB-D, LiDAR, and IMUs within platforms like NVIDIA Isaac Sim, leveraging USD and ROS/ROS2 for integration.
- Utilize finite element analysis (FEA) tools including Ansys, Comsol, and Abaqus to simulate mechanical characteristics of robotic parts.
- Advance sim-to-real validation by applying domain randomization, noise modeling, and physics-based constraints to close the gap between simulated and actual environments.
- Create simulation workflows to generate synthetic datasets that aid AI model training and assessment.
- Collaborate with AI/ML engineers to incorporate simulation results into training frameworks, particularly for physics-informed machine learning models.
Required Qualifications
- Master’s degree or Ph.D. in Robotics, Mechanical Engineering, Computer Science, Physics, or a related discipline.
- At least 10 years of professional experience in robotics simulation, sensor modeling, or mechanical system validation.
- Strong skills in programming languages including Python and C++, and experience with ROS/ROS2.
- Hands-on experience with simulation ecosystems such as Isaac Sim or Gazebo and FEA software like Ansys, Comsol, or Abaqus.
- Knowledge of machine learning frameworks including PyTorch and TensorFlow, alongside scientific computing tools.
- Understanding of challenges and techniques involved in sim-to-real transfer and validation.
Preferred Additional Experience
- Contributions to open-source projects related to simulation or finite element analysis.
- Experience with sensor hardware calibration and signal processing techniques.
- Exposure to physics-based machine learning or reinforcement learning applications in robotics.
- Proven experience generating synthetic data to train AI models.
Why Work With Us?
Joining ADI’s Edge AI team means working at the forefront of creating genuinely intelligent systems capable of sensing, learning, and acting in real time. The environment is dynamic and collaborative, focused on impactful, reliable solutions with practical real-world applications.
Additional Information
Applicants other than U.S. citizens, permanent residents, or protected individuals may need to undergo export licensing clearance due to the handling of technical data tied to U.S. Department of Commerce and State export regulations.
Analog Devices embraces equal employment opportunity and fosters an inclusive culture welcoming individuals from diverse backgrounds regardless of race, color, religion, age, gender identity, sexual orientation, disability, or other protected statuses.
Work Conditions
- Role requires approximately 10% travel.
- The position operates on a standard day shift schedule.