Research Engineer, Mechanical

Gridware is a technology company focused on 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 mechanical 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 headquartered in San Francisco, California, and 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 help build the measurement and validation infrastructure for a new product that expands Gridware's edge grid monitoring and observability platform.

This role blends hands-on experimentation with fleet-scale data analysis. You will analyze novel measurements across our deployed fleet, design and run physical tests to validate what those measurements mean, and build the tools and workflows that connect the two. 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 experimentally, and identify where performance limitations exist.

 

 

Health, Dental & Vision (Gold and Platinum with some providers plans fully covered) 

Paid parental leave 

Alternating day off (every other Monday)

“Off the Grid”, a two week per year paid break for all employees. 

Commuter allowance 

Company-paid training 

In this role you will

• Analyze sensor-derived measurements across our fleet. Identify anomalies and characterize measurement behavior across device populations.

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

• When more data is needed to solve a problem, execute physical experiments to collect that data. Instrument test articles (e.g., strain gauges, load cells, IMUs), implement data acquisition systems, and prepare comparison datasets to characterize measurement error.

• Work closely with algorithm developers, product managers, and other engineers to move new capabilities from measurement to customer delivery.

• Communicate findings clearly through documentation, presentations, and technical write-ups.

• MS or PhD in Mechanical Engineering, Civil/Structural Engineering, Engineering Mechanics, or a closely related field. 

• 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 systematically characterizing measurement system performance.

Experience with wood mechanics, timber engineering, or natural/biological materials. 

Experience with geotechnical or foundation mechanics (soil-structure interaction). 

Familiarity with GIS tools or spatial analysis for infrastructure data. 

Experience operationalizing analytical workflows into production systems (dashboards, automated reporting, alerting).