Software Engineer, X-ray Source Development

ZEISS•Dublin, CA

2d•Onsite

About The Position

The Software Engineer supports the development, automation, testing, and continuous improvement of manufacturing and engineering processes for advanced X-ray source products. The position combines software development, manufacturing automation, data analysis, and hands-on engineering support to improve production efficiency, product reliability, test capability, and data-driven decision-making. The engineer develops tools that automate production and test activities, acquires and processes engineering data, and provides analytical capabilities to characterize product performance and reliability. The role works closely with manufacturing, test, reliability, and engineering teams, and may support production equipment and X-ray source testing as needed. The role develops and maintains software tools for manufacturing, engineering, and production-test automation; creates data-acquisition and data-processing solutions; and analyzes production, calibration, reliability, and product-performance data. It also supports production-test execution and troubleshooting, automated and semi-automated test equipment, reliability investigations, production traceability, and process improvement. The position is based in Dublin, California.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, or a related technical discipline.
Programming experience in one or more general-purpose programming languages.
Experience developing scripts, software tools, or automation applications.
Understanding of software-development fundamentals, including debugging, testing, data structures, and structured problem solving.
Ability to work with structured engineering or manufacturing datasets and troubleshoot problems involving both software and hardware.
Strong analytical and technical problem-solving skills.
Ability to clearly document technical work and communicate results to engineering and manufacturing teams.
Comfort working in a hands-on engineering, laboratory, or manufacturing environment.

Nice To Haves

Experience with manufacturing automation, automated test systems, or engineering software development.
Experience developing automated data-acquisition or test-analysis tools and processing large engineering datasets.
Experience with statistical or engineering analysis tools such as JMP or similar platforms.
Familiarity with laboratory instrumentation, automated test equipment, or hardware/software interfaces.
Experience supporting high-technology manufacturing or complex electromechanical products.
Familiarity with production traceability, reliability analysis, calibration, characterization, or final-test processes.
Experience collaborating with manufacturing, mechanical, electrical, test, quality, or reliability engineers.

Responsibilities

Design, develop, test, and maintain software tools that automate manufacturing, engineering, and production-test processes.
Develop automated data-acquisition solutions for collecting engineering and production information from test equipment.
Develop scripts and utilities to process large quantities of production and test data.
Improve automation efficiency, reduce manual operations, and enable parallel data processing where appropriate.
Organize and correlate production data, test results, product history, operating hours, and component serial-number information.
Support development and implementation of production and final-test automation, including automated acquisition of vacuum, emitter, and heater metrics.
Support engineering and manufacturing teams in executing and troubleshooting production-test processes.
Operate and support automated and semi-automated test equipment for X-ray source conditioning, calibration, characterization, and final verification.
Assist with high-voltage conditioning, emitter testing, focus and spot calibration, astigmatism correction, randomizer testing, droop verification, write-to-flash operations, and other production-verification activities.
Monitor test progress, identify abnormal conditions, and troubleshoot issues during source testing.
Document source status, test conditions, anomalies, and completed manufacturing and test operations.
Support products through production testing, including Doghouse and Versa testing, in collaboration with engineers and manufacturing personnel.
Develop software for analyzing production, calibration, reliability, and product-performance data.
Aggregate and analyze data from multiple products, test stations, and historical production records.
Develop filtering and data-cleaning methods to distinguish valid production results from failed or non-representative test data.
Perform statistical analysis of product characteristics and manufacturing trends, including spot-size performance across products and operating energies.
Generate engineering datasets, plots, summaries, and other analytical outputs to support technical decisions.
Support evaluation of product performance across operating conditions and product populations.
Develop data-collection and analytical tools for product-reliability investigations.
Correlate component serial numbers with manufacturing history, operating hours, production results, field information, and test data.
Support engineering investigations through automated acquisition and analysis of source and component performance data.
Prepare structured datasets for additional statistical analysis using engineering analysis tools such as JMP.
Assist engineers in identifying patterns and relationships among manufacturing history, component configuration, and observed product behavior.
Identify opportunities for software and automation to reduce repetitive manual activities and improve manufacturing efficiency.
Support development and improvement of automated production workflows and integration of software tools into manufacturing and engineering environments.
Maintain clear documentation for software tools, data-analysis methods, test activities, and production status.
Collaborate with engineering and manufacturing teams to improve production-data traceability and accessibility.