About Analog Devices, Inc.
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, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world.
With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's PossibleTM.
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Responsibilities
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.
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.
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.
Develop simulation-driven workflows to produce high-quality synthetic datasets for training AI models, including perception, control, and physics-informed neural networks.
Collaborate with AI/ML engineers to integrate simulation outputs into model training pipelines, especially for physics-informed machine learning (PIML) applications.
Oversee on-site testing and validation of robotic platforms and sensor arrays, ensuring simulation fidelity and real-world performance alignment.
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 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.
Job Requirements
Job Req Type: Experienced.
Required Travel: Yes, 10% of the time.
Shift Type: 1st Shift/Days.
Export Licensing
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 U.S. Citizens, U.S. Permanent Residents, and protected individuals as defined by 8 U.S.C. ****b(a)(3) – may have to go through an export licensing review process.
EEO Statement
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.
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