Staff AI/ML Robotics Engineer

About Analog Devices

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.

Staff AI/ML Simulation Engineer

Location: Limerick
Team: Simulation Intelligence and Synthetic Data, Analog Devices Inc. (ADI)

Analog Devices’ Physical Intelligence organization is on a mission to redefine how intelligent systems perceive, understand, and interact with the physical world. We’re developing advanced simulation, synthetic data, and AI technologies that combine world-class semiconductor and sensor expertise with cutting-edge machine learning and physics-based modelling.

We are seeking a Staff AI/ML Simulation Engineer with expertise in physics-informed machine learning (PIML) to develop and enhance advanced simulation and synthetic data capabilities across a diverse range of physical systems.

In this role, you will leverage your expertise in machine learning, physical modelling, and simulation to develop technologies spanning robotics and robotic cognition, sensor simulation, signal-chain simulation, synthetic data generation, simulation for perception, and audio simulation. You will collaborate with a talented team of hardware, AI, and simulation engineers to develop high-fidelity simulation capabilities and help close the gap between simulated and real-world performance.

Responsibilities

• Design and implement physics-informed machine learning models for simulation, ensuring physical accuracy, realism, and computational efficiency.
• Develop and optimize simulation workflows that combine physics-based modelling with data-driven approaches across sensors, signal chains, robotics, perception, and other physical systems.
• Develop simulation and synthetic data capabilities for applications including robotics and robotic cognition, ToF, RGB-D, LiDAR, IMUs, audio, signal chains, and AI perception systems.
• Collaborate with hardware, AI, and simulation engineers to integrate simulation and synthetic data into real-world engineering and AI development workflows.
• Apply techniques such as noise modelling, domain randomization, surrogate modelling, physics-based constraints, and system identification to improve simulation fidelity and reduce simulation-to-reality gaps.
• Develop and validate simulation models against real-world measurements and system behaviour.
• Stay current with advancements in PIML, synthetic data, simulation, and Physical Intelligence, and contribute to the adoption of innovative approaches within the team.
• Utilize appropriate simulation, ML, and engineering platforms such as NVIDIA Isaac Sim, ROS/ROS2, Universal Scene Description (USD), and scientific computing frameworks where relevant.

Qualifications

• M.S. or Ph.D. in Physics, Robotics, Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
• 5+ years of experience in simulation, machine learning, physical modelling, or related engineering fields, with hands-on experience in physics-based or physics-informed modelling.
• Experience with advanced ML frameworks such as PyTorch or TensorFlow and scientific computing libraries.
• Demonstrated experience developing and deploying physics-informed, hybrid physics/ML, or data-driven models for simulation or related applications.
• Proficiency in Python and C++, with experience integrating ML models into simulation, engineering, or physical systems.
• Strong understanding of physical modelling, sensor physics, signal processing, or related physical-system fundamentals.
• Experience with synthetic data generation and validation of simulation models against real-world data.
• Experience applying simulation to AI development, such as perception, cognition, model training, system validation, or sim-to-real transfer.

Bonus Experience

• Contributions to open-source PIML, simulation, synthetic data, or scientific computing projects.
• Familiarity with sensor hardware design, signal chains, characterization, or calibration.
• Publications or project experience in physics-informed machine learning, simulation, synthetic data, robotics, sensor modelling, or related fields.
• Experience with NVIDIA Isaac Sim or other simulation platforms.
• Experience with numerical solvers including Ansys Workbench or COMSOL
• Experience with robotics, robotic cognition, autonomous systems, or simulation-based AI training.
• Experience with audio, acoustics, signal-chain, sensor, or multiphysics simulation.
• Proven track record of delivering simulation-driven workflows for AI model training, synthetic data generation, system validation, perception testing, or engineering development

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For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export  licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls.  As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.

Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.

Job Req Type: Experienced Required Travel: Yes, 10% of the time Shift Type: 1st Shift/Days