Grid Insights Architect (Principal Engineer)

About the role

Analog Devices' Energy Business Unit is looking for a highly experienced algorithm engineer and leader (Principal Engineer) to shape and deliver next-generation grid intelligence solutions. The role is hands-on technical leadership, with a clear path toward broader leadership responsibilities, and sits within the unit's focus on turning raw data from distribution grid assets into actionable intelligence.

Key responsibilities

• Own and drive the design and development of algorithms for power system monitoring and grid analytics (transformers, asset health, condition monitoring)
• Develop signal processing and machine learning techniques to extract insights from sensor data
• Architect and deploy solutions across the Edge-to-Cloud stack, including on resource-constrained embedded platforms
• Lead end-to-end solution development, from modelling and simulation through validation and production deployment
• Influence product and technology strategy and collaborate with hardware, firmware, data and application teams
• Work with grid equipment OEMs, utilities and industry partners, and mentor engineers

Required profile

• Ph.D. in Electrical Engineering, Computer Science, Power Systems, Mathematics, Physics or a related field (equivalent experience accepted)
• Minimum 6 years after the Ph.D. (12 years after a bachelor's degree) with practical algorithms deployed in production
• Record of increasing technical leadership and management responsibility
• Record of technical talks and influence, and patents or publications showing innovation
• Preferred: condition monitoring or predictive maintenance, industrial IoT or grid analytics platforms, prototype-to-production scaling, early leadership experience and willingness to travel 10% of the time

Required skills

• Power systems engineering and modelling of grid assets
• Signal processing and machine learning applied to real-world data
• Edge and cloud architectures, especially embedded environments
• Programming and modelling (Python, MATLAB/Simulink or embedded environments)
• Sensor data and physical system modelling (digital twins)