Principal AI/ML Robotics Simulation Engineer

Principal Ai/Ml Robotics Simulation Engineer

Location: Limerick Team: Edge Ai Group, Analog Devices Inc. (ADI)

Analog Devices' Edge AI team is on a mission to redefine how machines perceive and interact with the world. We're building real-time, intelligent systems that combine world-class sensor technology with cutting-edge AI — at the Edge, where milliseconds matter.

We are seeking a Principal AI/ML Robotics Simulation Engineer to lead the development of high-fidelity simulations and real-world validation workflows for robotic systems, including fixed-arm robots and humanoids. This role combines deep expertise in sensor simulation, mechanical modelling, and AI-driven data workflows to ensure our systems perform reliably in both virtual and physical environments.

You will collaborate across hardware, AI, and simulation teams to design custom sensor models, generate synthetic datasets, and validate simulation assets against real-world data. You will also contribute to the development of physics-informed machine learning models and mechanical simulations using FEA tools to enhance realism and predictive accuracy.

Responsibilities

• Sensor Simulation & Modelling: Design and implement custom sensor models (e.g., ToF, RGB-D, LiDAR, IMUs) in simulation platforms such as NVIDIA Isaac Sim, leveraging USD for integration.
• Mechanical Simulation & FEA: Apply traditional numerical simulation techniques (e.g., FEA) to model mechanical behaviour of robotic components. Use tools such as Ansys, Comsol, or Abaqus to simulate stress, deformation, and dynamic response.
• Sim-to-Real Alignment: Lead efforts to close the sim-to-real gap in robotics use-cases through domain randomization, noise modelling, and physics-based constraints. Validate simulation outputs against real-world data.
• Synthetic Data Generation: Develop simulation-driven workflows to produce high-quality synthetic datasets for training AI models, including perception, control, and physics-informed neural networks.
• ML Integration: Collaborate with AI/ML engineers to integrate simulation outputs into model training pipelines, especially for physics-informed machine learning (PIML) applications.
• Field Validation & Support: Oversee on-site testing and validation of robotic platforms and sensor arrays, ensuring simulation fidelity and real-world performance alignment.
• Innovation & Leadership: Stay current with advancements in robotics simulation, sensor technologies, and AI. Mentor junior engineers and guide the adoption of innovative simulation and validation techniques.

Qualifications

• Ph.D. or M.S. in Robotics, Mechanical Engineering, Computer Science, Physics, or a related field.
• 7+ years of experience in robotics simulation, sensor modelling, or mechanical validation.
• Deep expertise in simulation platforms (e.g., Isaac Sim, Gazebo) and sensor modelling.
• Strong proficiency in Python, C++, and software development best practices.
• Hands-on experience with FEA tools such as Ansys, Comsol, or Abaqus.
• Experience with advanced ML frameworks (e.g., PyTorch, TensorFlow) and scientific computing libraries.
• Proven track record of integrating simulation with real-world robotic systems.
• Experience with synthetic data generation and validation against physical benchmarks.
• Solid understanding of sim-to-real challenges and mitigation strategies.

Bonus Experience

• Experience implementing custom finite element solvers or contributing to open-source simulation frameworks.
• Familiarity with sensor hardware design, calibration, and signal processing.
• Publications or project experience in physics-informed machine learning or mechanical simulation.
• Experience with reinforcement learning (RL) and safe deployment of RL agents on physical robots.
• Proven ability to deliver simulation-driven workflows for AI model training, system validation, or perception testing.

Why Join Us?

Join ADI's Edge AI team to help create truly intelligent edge systems — where sensing, learning, and acting happen in real time. You'll work in a fast-paced, collaborative environment, solving hard problems with people who care about impact, reliability, and real-world performance.