Research Engineer, Mechanical

About GridwareGridware 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 Research Engineer to join our Physical R&D team. You will develop and operate the analytical infrastructure for a new product that expands Gridware’s edge grid monitoring and observability platform.

This role is primarily computational. You will analyze novel measurements across our deployed fleet, build tools and workflows for investigation and interpretation, and help operationalize new measurement products for delivery to utility customers. You will need deep physical intuition for the behavior of mechanical systems to interpret what the measurements mean in the real world, guide how we validate them, and identify where performance limitations exist.

Responsibilities

In this role you will

• Continuously analyze and investigate sensor-derived measurements across our fleet. Identify anomalies, characterize measurement behavior across device populations, and develop the workflows and tooling to do this at scale.

• Develop the computation, investigation, and reporting infrastructure that delivers measurement products to utility customers on a recurring basis.

• Build and maintain analysis pipelines—using Python, SQL, and related tools—to connect sensor outputs to physical metadata and develop narratives about where our measurements are and are not effective.

• Support internal and field validation efforts by performing data analysis, preparing comparison datasets, and helping characterize measurement error across device cohorts.

• Collaborate closely with algorithm developers, product managers, and engineers to operationalize new capabilities from measurement to customer delivery.

• Communicate findings clearly through documentation, presentations, and technical reports.

Required Skills

Required Skills

• MS or PhD in Mechanical Engineering, Civil/Structural Engineering, Engineering Mechanics, or a closely related field and 2+ years of relevant industry experience.

• Strong fundamentals in solid mechanics: beam theory, stress analysis, structural loading. Able to reason from first principles about what physical conditions produce the stresses and deflections we measure.

• Scientific computing: comfortable building analysis pipelines in Python, MATLAB, or equivalent to investigate and report system performance across large datasets.

• Experience with exploratory data analysis—identifying patterns, anomalies, and cohort-level behavior in noisy real-world data.

• Clear technical communication and documentation skills.

• Comfortable working on ambiguous technical problems and learning new domains quickly.

Relevant Depth Areas

You do not need to match every area below. Strong candidates will usually bring depth in two or more of these areas.

• Sensor data interpretation for physical systems: experience using sensor signals to draw conclusions about the physical state of a system—developing metrics, identifying signal features, or building monitoring workflows that translate raw measurements into actionable information.

• Statistical analysis of physical systems: experience with extreme value statistics, reliability analysis, survival analysis, or probabilistic methods applied to structural or mechanical systems.

• Fleet-scale data analysis and data engineering: experience working with sensor data from large deployed populations—including SQL-based analysis on structured telemetry, population-level trend identification, and data quality assessment across thousands of units.

• Validation and test design: experience designing validation studies, defining acceptance criteria, and