Senior Research Engineer, Structural Dynamics & Vibrations

About Gridware

Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware's advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation.

This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit www.

Gridware.io.

Role Description

We are seeking a creative, hands-on Senior Mechanical Research Engineer with significant vibration and dynamics experience to lead complex mechanical sensing problems on edge grid intelligence products with real-world impact.

You will become an expert in how our grid sensor signals - accelerometer, IMU, and other mechanical-measurement signals - behave in the real world across diverse infrastructure. You will investigate sensing performance via exploratory data analysis, dynamics modeling coupled with bench and full-scale testing. You will also support design improvements, and validate improvements using test infrastructure you develop. You will define mechanical sensing requirements, develop measurement-chain improvements, and help mature new mechanical sensing capabilities.

The focus is on understanding the mechanical phenomena that affect the grid, including measurement performance, validation, and technology transfer, rather than product design or implementation.

Responsibilities

In this role you will

• Develop mechanical sensing performance requirements. Select and evaluate new sensors for current and new mechanical sensing capabilities.
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• Root-cause mechanical sensor signal issues from fleet to bench: use fleet telemetry (time-series + metadata) to isolate failure patterns, modes and signatures, test hypotheses via dynamics modeling and benchtop reproduction, contribute to design solutions, and partner with HW, FW, and SW engineering to implement them.
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• Characterize and validate how diverse infrastructure types - different pole materials, geometries, and equipment configurations - affect mechanical signal behavior. Translate findings into design guidance, device installation requirements, monitoring and data aggregation methods.
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• Develop and own test methods to characterize and validate mechanical sensor performance across diverse and variable operating conditions.
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• Develop hardware-in-the-loop test infrastructure to reproduce real-world mechanical phenomena Gridware technology detects. Run hardware-in-the-loop tests to validate changes to our tech stack (from phenomena - hardware - automation - customer).
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• Develop signal quality observability for mechanical sensors: sensor trust metrics, quality scoring, and gating criteria for downstream uses of data.
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• Mentor team members. Raise the technical rigor of experiments, analysis, and validation work across the team.
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• Collaborate closely with product managers, data scientists, automation engineers, SW/HW/FW engineers, as well as non-technical teams such as customer success, field teams, and manufacturing.
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Required Skills

• Senior-level experience:
  PhD in Mechanical Engineering, Aerospace Engineering, Engineering Mechanics, or a closely related field plus 3+ years of relevant industry experience or MS in one of those fields plus 6+ years of relevant industry experience in owning open-ended, research-driven mechanical sensing or signal-quality problems.
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• Strong fundamentals in vibrations, dynamics, and structural response, with the ability to connect those fundamentals to real signals - extracting physical meaning through modal analysis, impact identification, source separation, frequency-domain feature extraction, and transient classification.
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• Track record of owning ambiguous sensing, signal-quality, or measurement-performance problems from characterizing through validation.
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• Scientific computing: comfortable writing…