Systems Quality Engineer

Short Summary

This Quality Assurance Engineer will test and improve complex rail automation systems that combine electrical equipment, mechanical components, sensors, controls, software, and AI. The person will create test plans, troubleshoot electromechanical problems, analyze defects and performance data, perform root cause analysis, validate corrective actions, and improve both automated and manual operating processes.

Specialization: Quality engineering for complex electromechanical automation systems

Clean Job Description

Position Summary

We are seeking an analytical, hands-on Quality Assurance Engineer to validate and improve advanced rail technology systems. These systems integrate electrical, mechanical, software, networking, controls, automation, and AI-enabled components.

This person will evaluate how systems perform under real operating conditions, identify differences between intended and actual performance, determine root causes, and drive measurable improvements in quality, reliability, safety, efficiency, and scalability.

This is a systems-level quality engineering position not a traditional software QA or defect-reporting role. The engineer will define system acceptance criteria, conduct hands-on testing, analyze performance data, troubleshoot electromechanical issues, and verify that corrective actions address underlying causes.

Key Responsibilities

System Testing and Validation

• Develop and execute test plans, test cases, validation procedures, and acceptance criteria for integrated electromechanical automation systems.
• Perform functional, integration, regression, system, and failure-condition testing.
• Test hardware, software, sensors, actuators, controls, and AI-enabled behavior as a complete system.
• Identify, reproduce, document, and track defects through resolution.
• Confirm that corrective actions resolve the underlying issue and prevent recurrence.
• Improve the effectiveness, repeatability, and measurement of testing processes.

Troubleshooting and Root Cause Analysis

• Diagnose electrical and electromechanical issues using schematics, measurements, system data, and structured troubleshooting methods.
• Evaluate interactions among electrical, mechanical, software, controls, AI models, and physical process variables.
• Distinguish symptoms from root causes and prioritize issues based on operational impact.
• Apply root cause analysis, 5 Whys, Pareto analysis, FMEA, DMAIC, and corrective and preventive action methods.
• Use evidence and quantified results to recommend corrective and preventive actions.

Data and Performance Analysis

• Analyze defect, failure, performance, and operational data to identify patterns and recurring issues.
• Compare system performance across deployments and operating conditions.
• Measure quality, reliability, throughput, cycle time, labor utilization, defect reduction, and other relevant indicators.
• Use data to identify improvement opportunities and validate results.

Process Improvement

• Evaluate manual and automated work processes for opportunities to improve safety, quality, throughput, consistency, reliability, and cost.
• Conduct structured observations and process analyses.
• Quantify opportunities for automation and operational improvement.
• Recommend practical changes to equipment, software, controls, deployment practices, and work processes.
• Support a proactive quality culture focused on preventing problems rather than only documenting them.

Collaboration and Documentation

• Work with electrical, mechanical, software, AI, DevSecOps, quality, and operations teams to resolve system-level problems.
• Explain technical findings to engineering and nontechnical stakeholders.
• Create test results, defect reports, investigation summaries, quality records, and improvement recommendations.
• Provide clear, evidence-based feedback that engineering and operations teams can act on.

Required Qualifications

• Bachelor s degree in Engineering, Quality Engineering, Industrial Engineering, Manufacturing Engineering, Systems Engineering, Computer Science, a related technical field, or equivalent relevant experience.
• Experience in quality engineering, systems testing, process engineering, manufacturing engineering, industrial automation, systems engineering, or a closely related field.
• Understanding of electrical and electromechanical systems, including sensors, actuators, digital and analog signals, I/O, relays, motors, power systems, and basic controls.
• Ability to read electrical schematics and troubleshoot electrical and electromechanical issues.
• Experience creating and executing test plans, documenting defects, analyzing results, and validating corrective actions.
• Experience with root cause analysis, structured problem-solving, and data-driven decision-making.
• Ability to understand interactions among electrical, mechanical, software, controls, and physical processes.
• Strong analytical skills and the ability to distinguish symptoms from root causes.
• Ability to analyze physical work processes and identify opportunities to improve automation, efficiency, quality, safety, or reliability.
• Strong written and verbal communication skills.

Preferred Qualifications

• Experience with Lean, Six Sigma, DMAIC, FMEA, statistical process control, process capability, or corrective and preventive action.
• Experience with industrial automation, robotics, manufacturing systems, machine controls, machine vision, PLCs, or other complex physical systems.
• Ability to read and understand basic C++ code or software logic; software development expertise is not required.
• Experience with time studies, process mapping, task analysis, work measurement, or automation-opportunity assessments.
• Experience using Excel, SQL, Python, MATLAB, or similar data-analysis tools.
• Experience with automated testing, scripting, test frameworks, version control, CI/CD, DevSecOps, or modern software-development practices.
• Exposure to AI and machine-learning systems and methods for testing adaptive system behavior.

Ideal Candidate

The ideal candidate is a curious, hands-on engineer who can examine a machine or system and determine:

• What should have happened
• What actually happened
• Where and why the failure occurred
• How to prevent it from happening again

The person should be comfortable working with physical equipment, electrical systems, performance data, test procedures, and software-controlled behavior. They do not need to be a software engineer, but they must be able to collaborate with software engineers and understand how software and control logic affect machine performance.

Relevant backgrounds may include quality engineering, manufacturing engineering, process engineering, systems engineering, industrial automation, robotics, or electromechanical troubleshooting.

Measures of Success

• Important defects are discovered earlier and resolved more permanently.
• Testing becomes more effective, measurable, repeatable, and reliable.
• Recurring failures decrease through stronger root cause analysis and corrective action.
• Manual and automated processes improve through structured observation and data analysis.
• Engineering teams receive clear, actionable findings that improve system reliability, quality, safety, and throughput.