Sr. Software QA Engineer

Bring more to life.

Are you ready to accelerate your potential and make a real difference within life sciences, diagnostics and biotechnology?

At Molecular Devices, one of Danaher’s 15+ operating companies, our work saves lives—and we’re all united by a shared commitment to innovate for tangible impact. 

You’ll thrive in a culture of belonging where you and your unique viewpoint matter. And by harnessing Danaher’s system of continuous improvement, you help turn ideas into impact – innovating at the speed of life.

Join Molecular Devices and help drive scientific discovery for life science customers in academia, biotech, pharma, and government. Our automated and AI-enabled technology empowers researchers to tackle complex questions and gain deep insights, accelerating the development of safer, more effective therapeutics. As part of our team—rooted in collaboration, authenticity, and innovation—you’ll ultimately contribute to groundbreaking science that enhances lives globally and shapes a healthier future for all.

Learn about the Danaher Business System which makes everything possible.

The Senior Software QA Engineer is responsible for driving software quality strategy and test automation for key Molecular Devices software products, from sprint-level execution through full release cycles. This role defines test architecture, coverage models, quality standards, CI/CD quality gates, and data-driven quality metrics to reduce release risk and improve product readiness across complex life-science software workflows.

This position reports to the Software Engineering Manager and is part of the R&D – Software Engineering team located in Downingtown, PA and will be an on-site role.

In this role, you will have the opportunity to:

• Own and drive QA strategy for key software products, including test architecture, coverage models, and quality standards across Agile sprint execution and product release cycles.

• Establish and enforce testing standards and best practices across coding guidelines, test documentation, TeamCity CI/CD quality gates, and Jira defect lifecycle management.

• Champion quality early in the development lifecycle by participating in requirements reviews, architecture discussions, and sprint grooming for HCS.ai and IN Carta® workflows.

• Mentor junior and mid-level QA engineers through test case reviews, test script reviews, technical guidance, and knowledge sharing.

• Drive continuous improvement of QA processes using Danaher Business System principles to reduce waste, rework, and systemic quality gaps, while collaborating with cross-functionally with software engineers, product owners, application scientists, and hardware teams.

The essential requirements of the job include:

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field; Master’s degree preferred.

• 5+ years experience in software quality assurance, including ownership of test strategy and automation architecture for complex software products.

• Hands-on proficiency with modern end-to-end automation frameworks such as Cypress, Playwright, or equivalent tools, plus scripting experience in TypeScript, Python, or C#.

• Experience configuring or using CI/CD pipelines such as TeamCity, Jenkins, or GitHub Actions to support quality gates, build health monitoring, and pipeline reliability.

• Advanced experience using Jira for defect lifecycle management, test planning, sprint reporting, and cross-team quality visibility.

Travel, Motor Vehicle Record & Physical/Environment Requirements:

• Ability to work in an office and laboratory/instrument environment as needed to support software verification and instrument-software integration testing.

• Occasional travel may be required for cross-site collaboration, product validation, or team/customer engagement; specific travel requirements to be confirmed by the hiring manager.

It would be a plus if you also possess previous experience in:

• HCS.ai, IN Carta®, or similar high-content imaging and analysis software platforms in a life science or laboratory automation environment.

• Hardware-software integration testing for lab instrument systems such as imaging platforms, automated liquid handlers, or robotic workflows.

• Professional use of AI-assisted development tools such as GitHub Copilot for test script generation, refactoring, migration support, or cove