Senior Quality Engineer

TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.

TetraScience is the category leader in this vital new market. In the last year alone, the world's dominant players in compute, cloud, data, and AI infrastructure have converged on TetraScience as the de facto standard, entering into co-innovation and go-to-market partnerships: Latest News and Announcements | TetraScience Newsroom

In connection with your candidacy, you will be asked to carefully review “The Tetra Way,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you.

It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building. If you join us, you will be expected to embody its contents daily.

We are adding a Senior Quality Engineer to the squads that build and deliver scientific use cases directly for a top-five global pharmaceutical customer. You will typically be the only quality engineer in a squad of developers, scientists and data engineers — owning quality for your use cases end to end, and trusted to run with them.

As a Senior Quality Engineer, you are the quality authority for your use cases. Your responsibilities will include:

•     Own quality for your use cases and sign off their releases. Hold QE sign-off authority, including the decision to withhold it, against the criteria Compliance sets.

•     Verify that what we built is what the customer asked for. Trace delivered work back to the documented requirements and make sure the evidence actually demonstrates them — not that a test merely ran.

•     Make sure the SDLC is followed. Requirements, design review, test evidence and traceability are in place and in the right order, in a validated environment.

•     Build and own test automation. Design robust, workflow-based automated tests across UI, API and database layers in our Playwright framework, and build them to be maintained and extended rather than rewritten.

•     Keep improving how we test. Continuously raise the test and automation approach — coverage, cycle time, and where quality is known in the development cycle — rather than running the same suite faster.

•     Use AI to do all of this better. Build automation with AI assistance, use it to generate and strengthen test cases and to improve the quality process itself, and apply judgement about where AI-generated work can and cannot stand as release evidence.

•     Contribute to verifying the AI in your use cases. Help establish that evaluation thresholds were set before results were known, that test data was genuinely held out, and that the claims made match what was measured.

•     Shape decisions before code is written. Raise testability, failure modes and risk in the squad while they are still cheap to change, using escape and defect patterns as your evidence.

•     Own the hard testing. Performance, security, tenant isolation and resiliency, including failure and degradation behaviour, balanced against functional, integration and regression work. Test manually and exploratorily where that is the right tool — automation is a means to coverage and cycle time, not an end in itself, and the failures nobody wrote a requirement for are usually found by hand.

•     Work directly with the customer's context. Collaborate with scientists, data engineers, solution architects, product managers and application developers across distributed teams, and ground what you test in how the customer actually uses the product.

•     Self-manage. Estimate, prioritize and plan your own QE work, manage your delivery, resolve your own blockers, and escalate the right things at the right time.

•     8+ years in quality engineering or SDET roles. Depth matters here, because the role depends on having seen enough systems fail to anticipate how this one will.

•     Proven ownership of quality for a product area independently — you have been the person a team relied on, not one of several testers on a plan someone else wrote.

•     Strong knowledge of software QA methodologies, tools and processes across the full SDLC, in an Agile/Scrum environment.

•     Proven expertise designing comprehensive test plans and test cases for evolving requirements, and reporting test runs and defects meticulously.

•     Proficiency in Python, Java or JavaScript, and deep hands-on experience with Playwright or equivalent for workflow-based end-to-end testing. You have built automation frameworks, not only written tests inside one.

•     Advanced SQL and a strong understanding of database systems, for querying and verifying the integrity and correctness of scientific data; exposure to warehouse architectures such as Databricks is an advantage.

•     Comfort integrating tests into CI/CD — pipeline stages and quality gates in GitHub Actions or equivalent.

•     Strong, practical use of AI tooling for building automation, generating test cases and improving quality process, with good judgement about its output. Deep AI/ML specialization is not required; willingness to learn enough to verify the AI features in your use cases is.

•     Experience with RESTful APIs, data pipelines, web applications and microservices on AWS, and with test management tooling such as Jira and Zephyr Scale.

•     Experience producing and being accountable for release evidence in a validated environment, working to the standards Compliance defines. You do not need to be a regulatory expert — Compliance owns the interpretation, you own the evidence that satisfies it.

•     Curiosity about the underlying science, and the ability to learn a scientific use case and the instruments behind it well enough to judge whether an output is correct. A science background or experience with scientific instruments is an advantage.

•     BS/MS in Computer Science, Engineering, Data Science, a life science, or equivalent experience.

•     Proactive and self-motivated, with the ability to thrive in a dynamic, fast-paced environment.

Join TetraScience and contribute to shaping the future of scientific research through innovative and high-quality software solutions!